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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'text'})
This happened while the json dataset builder was generating data using
hf://datasets/gss1147/Self_Q_and_A_dataset/hf_qa_training_250_hf_text.jsonl (at revision 940e27b344ef73ba4ad4c23f7a58d8a97804c389)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
id: int64
instruction: string
input: string
output: string
source: string
source_file: string
source_page: int64
evidence: string
text: string
to
{'id': Value('int64'), 'instruction': Value('string'), 'input': Value('string'), 'output': Value('string'), 'source': Value('string'), 'source_file': Value('string'), 'source_page': Value('int64'), 'evidence': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1339, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 972, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'text'})
This happened while the json dataset builder was generating data using
hf://datasets/gss1147/Self_Q_and_A_dataset/hf_qa_training_250_hf_text.jsonl (at revision 940e27b344ef73ba4ad4c23f7a58d8a97804c389)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id
int64 | instruction
string | input
string | output
string | source
string | source_file
string | source_page
int64 | evidence
string |
|---|---|---|---|---|---|---|---|
1
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Extract the training configuration described here and rewrite it as a concrete LoRA/TTT implementation checklist.
|
Checklist: (1) Build the adaptation dataset exactly as specified in the passage; (2) Insert LoRA adapters into the stated projection/MLP modules; (3) Train for the stated number of epochs/steps with the stated batch size and learning rate; (4) Enforce any stated cost/step budget; (5) Evaluate on the stated held-out tasks and record success rate; (6) Keep only configurations that meet the reward/cost criterion for further optimization. Parameters found in the passage: than=375; reward=1.
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SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 18
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protocol. To encourage efficient adaptation, we discarded configurations requiring more than 375 training steps, retained only configurations with reward 1 and acceptable cost for LoRA-based SFT. SFT for TTT was done with the following hyperparameters:
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2
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: Algorithm=1; Loop=1.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 16
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The overall training procedure of the Self-Evolving Embodied Agent is as follows (Algorithm 1). Algorithm 1:Self-Evolving Framework Training Loop 1:Initialize:Embodied Agent πθold←π ref, MCTS parameters, Tree-GRPO hyperparameters
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3
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 21
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Figure 10: Performance comparison of Tree-GRPO against MCTS+DPO and MCTS+SFT across training iterations. G.3 Comparison with Different Algorithms
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4
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 21
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training iterations. G.3 Comparison with Different Algorithms We investigate the comparative performance of Tree-GRPO against two established baseline methods:
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5
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 23
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G.6 Evaluating Data Quality from Iterative MCTS + GRPO via Supervised Fine-Tuning To assess the evolution of data quality throughout the Iterative MCTS + GRPO training process, we conducted an auxiliary experiment. We aimed to determine if data collected in later iterations
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6
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 21
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We investigate the comparative performance of Tree-GRPO against two established baseline methods: MCTS integrated with DPO (MCTS+DPO) and MCTS with SFT (MCTS+SFT). As illustrated in Figure 10, Tree-GRPO consistently outperforms both baselines across training iterations on the
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7
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What dataset split/evaluation protocol is specified here, and how would you implement it to avoid contamination?
|
Implement the split exactly as described: keep training and evaluation sets disjoint, and evaluate on the specified split (e.g., test-unseen / held-out subset). Add a leakage check by hashing identifiers (titles/contexts) across splits and reject any overlaps. Report metrics on the specified evaluation split only, and log the split manifest for reproducibility.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 19
|
Figure 8: Performance comparison of SEEA-R1 across different tasks in ALFWorld over training iterations. Left: Success rate across tasks. Right: Average number of steps taken to complete tasks. F Dataset Details
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8
|
Extract the training configuration described here and rewrite it as a concrete LoRA/TTT implementation checklist.
|
Checklist: (1) Build the adaptation dataset exactly as specified in the passage; (2) Insert LoRA adapters into the stated projection/MLP modules; (3) Train for the stated number of epochs/steps with the stated batch size and learning rate; (4) Enforce any stated cost/step budget; (5) Evaluate on the stated held-out tasks and record success rate; (6) Keep only configurations that meet the reward/cost criterion for further optimization. Parameters found in the passage: over=30.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 22
|
Figure 11: Learning curve of Tree-GRPO over 30 training iterations. exploration, enables more stable and effective updates, leading to superior sample efficiency and
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9
|
Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
|
Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds. Parameters found in the passage: collecting=512.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 7
|
data sizes and training hyperparameters. For each iteration, the model is trained on newly collected data for one epoch as steps is 4. The total number of iterations is not fixed and proceeds until convergence. Specifically, for Tree-GRPO, model updates are performed after collecting 512 valid
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10
|
From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 8
|
mechanisms. 4.2.3 Comparative Study of Training Algorithms To validate our proposed Tree-GRPO, we compared it against MCTS combined with DPO and SFT
|
|
11
|
What dataset split/evaluation protocol is specified here, and how would you implement it to avoid contamination?
|
Implement the split exactly as described: keep training and evaluation sets disjoint, and evaluate on the specified split (e.g., test-unseen / held-out subset). Add a leakage check by hashing identifiers (titles/contexts) across splits and reject any overlaps. Report metrics on the specified evaluation split only, and log the split manifest for reproducibility. Parameters found in the passage: comprising=3321.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 19
|
iterations. Left: Success rate across tasks. Right: Average number of steps taken to complete tasks. F Dataset Details ALFWorld: The ALFWorld dataset is structured into a training set comprising 3321 games and
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12
|
From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: Qwen=2.5.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 24
|
orange line tracks the success rate of the Iterative MCTS + GRPO policy over its training iterations. The blue bars represent the success rate of the Qwen2.5-VL-7B-Instruct model after SFT using data collected from the corresponding Iterative MCTS + GRPO iteration (filtered for Advantage > 0).
|
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13
|
From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 8
|
4.2.3 Comparative Study of Training Algorithms To validate our proposed Tree-GRPO, we compared it against MCTS combined with DPO and SFT on ALFWorld. As shown in Figure 5, Tree-GRPO (blue line) consistently achieves superior task
|
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14
|
From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 24
|
Figure 13: Success Rate Comparison: Iterative MCTS + GRPO vs. SFT on Generated Data. The orange line tracks the success rate of the Iterative MCTS + GRPO policy over its training iterations.
|
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15
|
From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: runs=30; depth=30.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 7
|
Probabilistic Expansion(50% chance to expand all Kactions per node, reducing redundancy) and Strict Path Budget(hard limit L=5 on full K-expansions per path, constraining search space). Each MCTS runs 30 iterations with max simulation depth 30.
|
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16
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 21
|
MCTS integrated with DPO (MCTS+DPO) and MCTS with SFT (MCTS+SFT). As illustrated in Figure 10, Tree-GRPO consistently outperforms both baselines across training iterations on the ALFWorld unseen test set. Tree-GRPO reached a peak success rate of 32.46%, which is significantly
|
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17
|
From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: Qwen=2.5.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 24
|
Figure 13: Success Rate Comparison: Iterative MCTS + GRPO vs. SFT on Generated Data. The orange line tracks the success rate of the Iterative MCTS + GRPO policy over its training iterations. The blue bars represent the success rate of the Qwen2.5-VL-7B-Instruct model after SFT using data
|
|
18
|
From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: Algorithm=1; Loop=1; hyperparameters=2.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 16
|
Algorithm 1:Self-Evolving Framework Training Loop 1:Initialize:Embodied Agent πθold←π ref, MCTS parameters, Tree-GRPO hyperparameters 2:foriteration I= 1,2, . . . do
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19
|
From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: iter_=1; iter_=12; approximately=0.8; about=0.7.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 22
|
compared to earlier iterations (cooler colors, e.g., iter_1 ) across various MCTS search step counts. For instance, at 15 MCTS search steps, iter_12 attains an average reward of approximately 0.8, whereas iter_1 reaches about 0.7. This performance gap tends to widen with an increasing number
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20
|
From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: iter_=12; iter_=1; approximately=0.8.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 22
|
(warmer colors, e.g., iter_12 ) consistently achieve higher average cumulative maximum rewards compared to earlier iterations (cooler colors, e.g., iter_1 ) across various MCTS search step counts. For instance, at 15 MCTS search steps, iter_12 attains an average reward of approximately 0.8,
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21
|
Extract the training configuration described here and rewrite it as a concrete LoRA/TTT implementation checklist.
|
Checklist: (1) Build the adaptation dataset exactly as specified in the passage; (2) Insert LoRA adapters into the stated projection/MLP modules; (3) Train for the stated number of epochs/steps with the stated batch size and learning rate; (4) Enforce any stated cost/step budget; (5) Evaluate on the stated held-out tasks and record success rate; (6) Keep only configurations that meet the reward/cost criterion for further optimization. Parameters found in the passage: the=50; for=2; rate=3e-4; rank=64; alpha=128; batch size=10.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 20
|
round of ReSTEM, we perform supervised finetuning on the 50 resulting prompt-completion pairs. Supervised finetuning here is done with batch size of 10, for 2 epochs, with learning rate 3e-4, using LoRA [72] with rank 64 and alpha 128, applied to all MLP and attention projection layers.
|
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22
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: the=25.37; the=16.79.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 21
|
Figure 10, Tree-GRPO consistently outperforms both baselines across training iterations on the ALFWorld unseen test set. Tree-GRPO reached a peak success rate of 32.46%, which is significantly higher than the 25.37% peak achieved by MCTS+DPO and the 16.79% peak of MCTS+SFT.
|
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23
|
From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: runs=30; depth=30; Qwen=2.5.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 7
|
Strict Path Budget(hard limit L=5 on full K-expansions per path, constraining search space). Each MCTS runs 30 iterations with max simulation depth 30. Training Setting.We use the Qwen2.5-VL-7B-Instruct [ 48] as the base model to build our embod-
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24
|
Extract the training configuration described here and rewrite it as a concrete LoRA/TTT implementation checklist.
|
Checklist: (1) Build the adaptation dataset exactly as specified in the passage; (2) Insert LoRA adapters into the stated projection/MLP modules; (3) Train for the stated number of epochs/steps with the stated batch size and learning rate; (4) Enforce any stated cost/step budget; (5) Evaluate on the stated held-out tasks and record success rate; (6) Keep only configurations that meet the reward/cost criterion for further optimization. Parameters found in the passage: for=2; rate=3e-4; rank=64; alpha=128; batch size=10; learning rate=3e-4.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 20
|
Supervised finetuning here is done with batch size of 10, for 2 epochs, with learning rate 3e-4, using LoRA [72] with rank 64 and alpha 128, applied to all MLP and attention projection layers. B.3 Synthetic Data Generation and Finetuning Details
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25
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 9
|
Search Algorithm Optimization.Standard MCTS has complexity O(T·D·K) (where T= training steps, D= search tree depth, K= actions per node). We reduce this to O(T·max(D, K)) via MCTS pruning as mentioned in Section 4.1.2.
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26
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 21
|
SEEA-R1 60.2 48.5 66.4 79.4 766 Figure 10: Performance comparison of Tree-GRPO against MCTS+DPO and MCTS+SFT across training iterations.
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27
|
From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 9
|
Our method achieves higher success rates with lower computational cost (especially in inference token usage) viatraining-stage algorithmic optimizationandlightweight inference design. Search Algorithm Optimization.Standard MCTS has complexity O(T·D·K) (where T=
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28
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
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cumulative maximum goal reward against MCTS search steps. The color gradient (blues for early iterations, reds for later ones) highlights the progression. This observed upward trend in average reward across self-evolution iterations strongly suggests that
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
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SEEA-R1 (arXiv:2506.21669v2)
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over baselines like MCTS+DPO and MCTS+SFT. –Analysis of the long-term performance of Tree-GRPO over extended training iterations. –Investigation into the impact of iterative self-evolution on MCTS performance and
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: iter_=1; toiter_=12.
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SEEA-R1 (arXiv:2506.21669v2)
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Each line represents a distinct self-evolution iteration (from iter_1 toiter_12 ), plotting the average cumulative maximum goal reward against MCTS search steps. The color gradient (blues for early iterations, reds for later ones) highlights the progression.
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
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SEEA-R1 (arXiv:2506.21669v2)
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token usage) viatraining-stage algorithmic optimizationandlightweight inference design. Search Algorithm Optimization.Standard MCTS has complexity O(T·D·K) (where T= training steps, D= search tree depth, K= actions per node). We reduce this to O(T·max(D, K))
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Summarize the compute/training-cost constraints described here and convert them into enforceable engineering limits.
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Convert the stated compute constraints into hard caps in code: maximum steps per run, maximum wall time, and maximum sampled candidates. Log GPU type/count, tokens/steps, sampling time vs training time, and enforce early termination when the stated limits are exceeded. Track median cost per successful run alongside accuracy to prevent trading quality for uncontrolled compute.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
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4.2.4 Efficiency Analysis Our method achieves higher success rates with lower computational cost (especially in inference token usage) viatraining-stage algorithmic optimizationandlightweight inference design.
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: follows=1.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
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iterative loop of two core cycles as follows:1.Data Evolution:The Policy Model interacts with the environment via MCTS from an initial state to generate the experience dataset, containing trajectories with derived Q-values, ground truth rewards from the environment, and rewards from the
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
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existing baselines, followed by an in-depth analysis and ablation studies to examine the effects of different training algorithms and our self-evolution reward design. 4.1 Experimental Setup
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: required=24; took=48.
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required 24 hours and PPO training took 48 hours under similar hardware settings. Algorithmic Optimization.Standard MCTS scales as O(bd)with branching factor band depth d. To improve scalability, SEEA-R1 introduces a pruning strategy combiningprobabilistic expansion
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What dataset split/evaluation protocol is specified here, and how would you implement it to avoid contamination?
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Implement the split exactly as described: keep training and evaluation sets disjoint, and evaluate on the specified split (e.g., test-unseen / held-out subset). Add a leakage check by hashing identifiers (titles/contexts) across splits and reject any overlaps. Report metrics on the specified evaluation split only, and log the split manifest for reproducibility. Parameters found in the passage: selected=11; selected=8.
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evaluation splits that are solvable with optimal TTT hyperparameters. Training Set:We selected 11 ARC tasks from the training set as the environment for RL optimization. Evaluation Set:We selected 8 distinct ARC problems from the evaluation set for measuring
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
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SEEA-R1 (arXiv:2506.21669v2)
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enhancing both agent performance and data generation. G.6 Evaluating Data Quality from Iterative MCTS + GRPO via Supervised Fine-Tuning To assess the evolution of data quality throughout the Iterative MCTS + GRPO training process,
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
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SEAL (arXiv:2506.10943)
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2506.10943.pdf
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mark [ 14], where the model leverages a set oftoolsto autonomously select both synthetic data augmentations and optimization hyperparameters (e.g., learning rate, training epochs, selective loss computation over token types). Our experiments demonstrate that automatic selection and configura-
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
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fine-tuning framework designed to support self-evolving capabilities in embodied agents. (2) By introducingTree-GRPO, we augment GRPO with MCTS to enable dense and interpretable credit assignment across multi-step trajectories, (3) We replace handcrafted reward signals withMGRM, a
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What dataset split/evaluation protocol is specified here, and how would you implement it to avoid contamination?
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Implement the split exactly as described: keep training and evaluation sets disjoint, and evaluate on the specified split (e.g., test-unseen / held-out subset). Add a leakage check by hashing identifiers (titles/contexts) across splits and reject any overlaps. Report metrics on the specified evaluation split only, and log the split manifest for reproducibility. Parameters found in the passage: selected=11.
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SEAL (arXiv:2506.10943)
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To enable controlled evaluation, we curated a small set of ARC problems from the training and evaluation splits that are solvable with optimal TTT hyperparameters. Training Set:We selected 11 ARC tasks from the training set as the environment for RL optimization.
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds. Parameters found in the passage: Algorithm=1.
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SEEA-R1 (arXiv:2506.21669v2)
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B Algorithm Details The overall training procedure of the Self-Evolving Embodied Agent is as follows (Algorithm 1). Algorithm 1:Self-Evolving Framework Training Loop
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
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SEEA-R1 (arXiv:2506.21669v2)
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G.3 Comparison with Different Algorithms We investigate the comparative performance of Tree-GRPO against two established baseline methods: MCTS integrated with DPO (MCTS+DPO) and MCTS with SFT (MCTS+SFT). As illustrated in
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
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introducingTree-GRPO, we augment GRPO with MCTS to enable dense and interpretable credit assignment across multi-step trajectories, (3) We replace handcrafted reward signals withMGRM, a multi-modal generative reward model that rewards task completion. (4) We achieve newstate-of-the-
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Summarize the compute/training-cost constraints described here and convert them into enforceable engineering limits.
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Convert the stated compute constraints into hard caps in code: maximum steps per run, maximum wall time, and maximum sampled candidates. Log GPU type/count, tokens/steps, sampling time vs training time, and enforce early termination when the stated limits are exceeded. Track median cost per successful run alongside accuracy to prevent trading quality for uncontrolled compute.
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model sizes and compute budgets: proxy metrics offer dramatically lower cost, and with refinement, they may even surpass the “true” reward of directly optimizing for post-finetuning performance. B.11 Prompting
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds. Parameters found in the passage: Algorithm=1.
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SEEA-R1 (arXiv:2506.21669v2)
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pipeline. B Algorithm Details The overall training procedure of the Self-Evolving Embodied Agent is as follows (Algorithm 1).
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
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simulator. Figure 8: Performance comparison of SEEA-R1 across different tasks in ALFWorld over training iterations. Left: Success rate across tasks. Right: Average number of steps taken to complete tasks.
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: Qwen=2.5; required=24; took=48.
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SEEA-R1 (arXiv:2506.21669v2)
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its performance surpasses proprietary models such as GPT-4o. For comparison, Qwen2.5 fine-tuning required 24 hours and PPO training took 48 hours under similar hardware settings. Algorithmic Optimization.Standard MCTS scales as O(bd)with branching factor band depth d.
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds. Parameters found in the passage: Algorithm=1.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
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We outline the overall training procedure of our Self-Evolving Embodied Agent (SEEA-R1), as detailed in Algorithm 1. •Appendix C: Embodied Agent Formulation
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: with=50.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
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and apath budget, effectively reducing complexity to O(pLk) . Each node expands its kactions with 50% probability, and fullk-expansions are limited toL= 5per path. Inference Efficiency.MCTS is used only during training. At inference time, SEEA-R1 performs
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds. Parameters found in the passage: Algorithm=1.
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SEEA-R1 (arXiv:2506.21669v2)
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•Appendix B: Algorithm Details We outline the overall training procedure of our Self-Evolving Embodied Agent (SEEA-R1), as detailed in Algorithm 1.
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
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SEEA-R1 (arXiv:2506.21669v2)
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G.4 Long-Term Performance of Tree-GRPO To thoroughly assess the long-term effectiveness of Tree-GRPO, we extended the training duration of the model to 30 iterations. As illustrated in Figure 11, the success rate on the ALFWorld
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds. Parameters found in the passage: initial=11.57.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
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To thoroughly assess the long-term effectiveness of Tree-GRPO, we extended the training duration of the model to 30 iterations. As illustrated in Figure 11, the success rate on the ALFWorld unseen test set demonstrates a consistent and substantial increase, rising from an initial 11.57% to
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds. Parameters found in the passage: Model=10.
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SEEA-R1 (arXiv:2506.21669v2)
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9:// Model Evolution: Co-refining Policy Model and Reward Model 10:Update reward model parameters by using the GRPO algorithm using Dθold: 11:Update agent parameters θby optimizing the Tree-GRPO objective J(θ) usingDθold:
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What dataset split/evaluation protocol is specified here, and how would you implement it to avoid contamination?
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Implement the split exactly as described: keep training and evaluation sets disjoint, and evaluate on the specified split (e.g., test-unseen / held-out subset). Add a leakage check by hashing identifiers (titles/contexts) across splits and reject any overlaps. Report metrics on the specified evaluation split only, and log the split manifest for reproducibility.
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SEAL (arXiv:2506.10943)
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the SQuAD dataset v1.1 [ 13] for the task of answering questionswithoutthe passage in-context. We use the training set for RL training and a 200-article subset of the evaluation set for evaluation. Within the training set and evaluation set, there are some overlapping topics of passages, but there
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: set=10.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
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MGRM relies on Test test Reinforcement Learning (TTRL) [ 37] and GRPO: the policy generates K (set 10 in environments) diverse trajectories per initial state s0(via MCTS exploration), and MGRM’s majority-voted predictions across these trajectories form pseudo-GT for s0. MGRM is then trained
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
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used. For DPO, 512 positive-negative trajectories pairs are sampled from the same parent node in the MCST. When training the multimodal generative reward model (MGRM) via GRPO, the group size is set to 10, which equals to the vote num using TTRL). All training adopt the same hyperparameters:
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds. Parameters found in the passage: collecting=512.
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SEEA-R1 (arXiv:2506.21669v2)
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data for one epoch as steps is 4. The total number of iterations is not fixed and proceeds until convergence. Specifically, for Tree-GRPO, model updates are performed after collecting 512 valid samples (i.e., with non-zero advantage values). For SFT, 512 trajectories with positive advantages are
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What dataset split/evaluation protocol is specified here, and how would you implement it to avoid contamination?
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Implement the split exactly as described: keep training and evaluation sets disjoint, and evaluate on the specified split (e.g., test-unseen / held-out subset). Add a leakage check by hashing identifiers (titles/contexts) across splits and reject any overlaps. Report metrics on the specified evaluation split only, and log the split manifest for reproducibility. Parameters found in the passage: comprising=3321.
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2506.21669v2.pdf
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ALFWorld: The ALFWorld dataset is structured into a training set comprising 3321 games and a test set, further partitioned into test-seen (140 games) and test-unseen (134 games) splits. This distinction is crucial for assessing out-of-distribution (OOD) generalization, as unseen tasks introduce
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
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SEEA-R1 (arXiv:2506.21669v2)
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Appendix N.4. This allows us to leverage the DeepSeek-R1-Zero [ 1] training paradigm to employ the GRPO [2] for reinforcement learning , supporting two training paradigms tailored to scenarios with or without ground truth (GT) rewards.
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds. Parameters found in the passage: reward=1.
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final output tokens). Each configuration was evaluated via test-time training (TTT), and assigned a binary reward: 1 if the adapted model produced the correct solution, 0 otherwise using Akyürek et al. [36]’s evaluation
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Summarize the compute/training-cost constraints described here and convert them into enforceable engineering limits.
|
Convert the stated compute constraints into hard caps in code: maximum steps per run, maximum wall time, and maximum sampled candidates. Log GPU type/count, tokens/steps, sampling time vs training time, and enforce early termination when the stated limits are exceeded. Track median cost per successful run alongside accuracy to prevent trading quality for uncontrolled compute.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 15
|
We specify the experimental hardware (8 NVIDIA A100 80GB GPUs) and frameworks (MS-Swift for training, vLLM for inference), quantify SEEA-R1’s time costs (sampling, policy training, total) under "with/without GT reward" configurations, and analyze efficiency
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62
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
|
Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds. Parameters found in the passage: for=9; Model=10.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 16
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8:end for 9:// Model Evolution: Co-refining Policy Model and Reward Model 10:Update reward model parameters by using the GRPO algorithm using Dθold:
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63
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Summarize the compute/training-cost constraints described here and convert them into enforceable engineering limits.
|
Convert the stated compute constraints into hard caps in code: maximum steps per run, maximum wall time, and maximum sampled candidates. Log GPU type/count, tokens/steps, sampling time vs training time, and enforce early termination when the stated limits are exceeded. Track median cost per successful run alongside accuracy to prevent trading quality for uncontrolled compute. Parameters found in the passage: for=36; Qwen=2.5.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 17
|
D.1 Training and Efficiency Analysis Training Cost.SEEA-R1 was trained for 36 hours on 8 ×A100 GPUs, a modest budget considering its performance surpasses proprietary models such as GPT-4o. For comparison, Qwen2.5 fine-tuning
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64
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
|
Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 16
|
10:Update reward model parameters by using the GRPO algorithm using Dθold: 11:Update agent parameters θby optimizing the Tree-GRPO objective J(θ) usingDθold: 12: θ←Tree-GRPO_Update(θ old,Dθold,J, π ref)
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65
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: set=10.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 6
|
Without GT (Self-supervised Paradigm): In GT-free scenarios (e.g., real-world environments), MGRM relies on Test test Reinforcement Learning (TTRL) [ 37] and GRPO: the policy generates K (set 10 in environments) diverse trajectories per initial state s0(via MCTS exploration), and MGRM’s
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66
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Extract the training configuration described here and rewrite it as a concrete LoRA/TTT implementation checklist.
|
Checklist: (1) Build the adaptation dataset exactly as specified in the passage; (2) Insert LoRA adapters into the stated projection/MLP modules; (3) Train for the stated number of epochs/steps with the stated batch size and learning rate; (4) Enforce any stated cost/step budget; (5) Evaluate on the stated held-out tasks and record success rate; (6) Keep only configurations that meet the reward/cost criterion for further optimization. Parameters found in the passage: run=2; sample=5; temperature=1; over=3.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 20
|
We run 2 rounds of ReSTEMtraining [ 40]. On each round, we take a batch of 50 context-questions- answers triples from the SQuAD training set. For each context, we sample 5 self-edit generations at temperature 1. We evaluate each self-edit over 3 random seeds, training on the sequences and then
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67
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What dataset split/evaluation protocol is specified here, and how would you implement it to avoid contamination?
|
Implement the split exactly as described: keep training and evaluation sets disjoint, and evaluate on the specified split (e.g., test-unseen / held-out subset). Add a leakage check by hashing identifiers (titles/contexts) across splits and reject any overlaps. Report metrics on the specified evaluation split only, and log the split manifest for reproducibility.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 20
|
We use the Qwen-2.5-7B base model [ 5] in the knowledge incorporation experiments. We repurpose the SQuAD dataset v1.1 [ 13] for the task of answering questionswithoutthe passage in-context. We use the training set for RL training and a 200-article subset of the evaluation set for evaluation.
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68
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What dataset split/evaluation protocol is specified here, and how would you implement it to avoid contamination?
|
Implement the split exactly as described: keep training and evaluation sets disjoint, and evaluate on the specified split (e.g., test-unseen / held-out subset). Add a leakage check by hashing identifiers (titles/contexts) across splits and reject any overlaps. Report metrics on the specified evaluation split only, and log the split manifest for reproducibility. Parameters found in the passage: comprising=3321.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 19
|
F Dataset Details ALFWorld: The ALFWorld dataset is structured into a training set comprising 3321 games and a test set, further partitioned into test-seen (140 games) and test-unseen (134 games) splits. This
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69
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Extract the training configuration described here and rewrite it as a concrete LoRA/TTT implementation checklist.
|
Checklist: (1) Build the adaptation dataset exactly as specified in the passage; (2) Insert LoRA adapters into the stated projection/MLP modules; (3) Train for the stated number of epochs/steps with the stated batch size and learning rate; (4) Enforce any stated cost/step budget; (5) Evaluate on the stated held-out tasks and record success rate; (6) Keep only configurations that meet the reward/cost criterion for further optimization. Parameters found in the passage: run=2; sample=5.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 20
|
B.2 RL Training Procedure We run 2 rounds of ReSTEMtraining [ 40]. On each round, we take a batch of 50 context-questions- answers triples from the SQuAD training set. For each context, we sample 5 self-edit generations at
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70
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Extract the training configuration described here and rewrite it as a concrete LoRA/TTT implementation checklist.
|
Checklist: (1) Build the adaptation dataset exactly as specified in the passage; (2) Insert LoRA adapters into the stated projection/MLP modules; (3) Train for the stated number of epochs/steps with the stated batch size and learning rate; (4) Enforce any stated cost/step budget; (5) Evaluate on the stated held-out tasks and record success rate; (6) Keep only configurations that meet the reward/cost criterion for further optimization.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 22
|
LoRA weights from context, using our evaluation setup. Table 7 reports results for both single- passage ( n=1) and continued pretraining ( n= 200 ). We use the Mistral-7B-based model [ 85] for Generative Adapter, since that was the closest model for comparison. All values are on the same
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71
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Extract the training configuration described here and rewrite it as a concrete LoRA/TTT implementation checklist.
|
Checklist: (1) Build the adaptation dataset exactly as specified in the passage; (2) Insert LoRA adapters into the stated projection/MLP modules; (3) Train for the stated number of epochs/steps with the stated batch size and learning rate; (4) Enforce any stated cost/step budget; (5) Evaluate on the stated held-out tasks and record success rate; (6) Keep only configurations that meet the reward/cost criterion for further optimization.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 22
|
We additionally compared with Generative Adapter [ 54], a hypernetwork approach that generates LoRA weights from context, using our evaluation setup. Table 7 reports results for both single- passage ( n=1) and continued pretraining ( n= 200 ). We use the Mistral-7B-based model [ 85] for
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72
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 9
|
training steps, D= search tree depth, K= actions per node). We reduce this to O(T·max(D, K)) via MCTS pruning as mentioned in Section 4.1.2. Inference Efficiency.MCTS is only used during training. At test time, SEEA-R1 uses fast ReAct-
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73
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Extract the training configuration described here and rewrite it as a concrete LoRA/TTT implementation checklist.
|
Checklist: (1) Build the adaptation dataset exactly as specified in the passage; (2) Insert LoRA adapters into the stated projection/MLP modules; (3) Train for the stated number of epochs/steps with the stated batch size and learning rate; (4) Enforce any stated cost/step budget; (5) Evaluate on the stated held-out tasks and record success rate; (6) Keep only configurations that meet the reward/cost criterion for further optimization. Parameters found in the passage: run=2.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 20
|
passages due to RL training. B.2 RL Training Procedure We run 2 rounds of ReSTEMtraining [ 40]. On each round, we take a batch of 50 context-questions-
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74
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Summarize the compute/training-cost constraints described here and convert them into enforceable engineering limits.
|
Convert the stated compute constraints into hard caps in code: maximum steps per run, maximum wall time, and maximum sampled candidates. Log GPU type/count, tokens/steps, sampling time vs training time, and enforce early termination when the stated limits are exceeded. Track median cost per successful run alongside accuracy to prevent trading quality for uncontrolled compute. Parameters found in the passage: ratio=0.05; batch size=128.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 7
|
is set to 10, which equals to the vote num using TTRL). All training adopt the same hyperparameters: a cosine annealing learning rate schedule (initial LR: 1e-6, warmup ratio: 0.05), batch size of 128, and KL divergence coefficient βof 0.0. Experiments are conducted on 8 NVIDIA A100 80GB GPUs
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75
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Summarize the compute/training-cost constraints described here and convert them into enforceable engineering limits.
|
Convert the stated compute constraints into hard caps in code: maximum steps per run, maximum wall time, and maximum sampled candidates. Log GPU type/count, tokens/steps, sampling time vs training time, and enforce early termination when the stated limits are exceeded. Track median cost per successful run alongside accuracy to prevent trading quality for uncontrolled compute. Parameters found in the passage: for=36.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 17
|
N(st, at)(5) D.1 Training and Efficiency Analysis Training Cost.SEEA-R1 was trained for 36 hours on 8 ×A100 GPUs, a modest budget considering
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76
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 9
|
SEEA-R1-7B (w/ GT reward)83.3 58.3 58.3 83.3 75.0 50.0 68.1 success rates (Figure 5a) and greater efficiency with fewer average steps (Figure 5b) than MCTS + DPO (purple line) and MCTS + SFT (red line). This demonstrates the enhanced performance of
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77
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
|
Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds. Parameters found in the passage: GRPO=44; GRPO=43.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 7
|
SEEA-R1 (w/o GT reward) 7B Tree-GRPO 44 60 73 58 26 26 44 SEEA-R1 (w/ GT reward) 7B Tree-GRPO 43 42 60 41 29 40 46 EmbodiedEval.To evaluate the generalization ability of SEEA-R1 beyond the training environment,
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78
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 23
|
This observed upward trend in average reward across self-evolution iterations strongly suggests that the models become more proficient at guiding MCTS towards successful outcomes. Consequently, trajectories sampled by MCTS using these more advanced models are of increasingly higher quality.
|
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79
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
|
Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 20
|
and exhibited less stable convergence compared to the largest configuration. These results collectively indicate that increasing the sample and batch sizes contributes to more stable and effective policy updates in GRPO-based training, likely due to reduced variance in gradient estimation, ultimately
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80
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 23
|
iterations, reds for later ones) highlights the progression. This observed upward trend in average reward across self-evolution iterations strongly suggests that the models become more proficient at guiding MCTS towards successful outcomes. Consequently,
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81
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
|
Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 20
|
indicate that increasing the sample and batch sizes contributes to more stable and effective policy updates in GRPO-based training, likely due to reduced variance in gradient estimation, ultimately leading to superior and more consistent performance on unseen tasks.
|
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82
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 2
|
augmentations and optimization hyperparameters (e.g., learning rate, training epochs, selective loss computation over token types). Our experiments demonstrate that automatic selection and configura- tion of these tools using SEAL enhances performance compared to both standard in-context learning
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83
|
From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 15
|
stability and success rate. –An algorithmic comparison demonstrating the superiority of our proposed Tree-GRPO over baselines like MCTS+DPO and MCTS+SFT.
|
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84
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Extract the training configuration described here and rewrite it as a concrete LoRA/TTT implementation checklist.
|
Checklist: (1) Build the adaptation dataset exactly as specified in the passage; (2) Insert LoRA adapters into the stated projection/MLP modules; (3) Train for the stated number of epochs/steps with the stated batch size and learning rate; (4) Enforce any stated cost/step budget; (5) Evaluate on the stated held-out tasks and record success rate; (6) Keep only configurations that meet the reward/cost criterion for further optimization.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 5
|
using LoRA. The updated model is evaluated on questions about the passagewithoutaccess to the original text, and the resulting accuracy serves as the reward signal for reinforcement learning. These self-generated statements form the training data for a supervised finetuning (SFT) update: we
|
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85
|
Summarize the compute/training-cost constraints described here and convert them into enforceable engineering limits.
|
Convert the stated compute constraints into hard caps in code: maximum steps per run, maximum wall time, and maximum sampled candidates. Log GPU type/count, tokens/steps, sampling time vs training time, and enforce early termination when the stated limits are exceeded. Track median cost per successful run alongside accuracy to prevent trading quality for uncontrolled compute.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 23
|
which edits improve its own performance. Both approaches appear promising for scaling to larger model sizes and compute budgets: proxy metrics offer dramatically lower cost, and with refinement, they may even surpass the “true” reward of directly optimizing for post-finetuning performance.
|
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86
|
Summarize the compute/training-cost constraints described here and convert them into enforceable engineering limits.
|
Convert the stated compute constraints into hard caps in code: maximum steps per run, maximum wall time, and maximum sampled candidates. Log GPU type/count, tokens/steps, sampling time vs training time, and enforce early termination when the stated limits are exceeded. Track median cost per successful run alongside accuracy to prevent trading quality for uncontrolled compute.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 6
|
grid resolution), and chained or repeated transformations. •Optimization parameters:learning rate, number of training epochs, and whether the loss is computed over all tokens or only output tokens.
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87
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Extract the training configuration described here and rewrite it as a concrete LoRA/TTT implementation checklist.
|
Checklist: (1) Build the adaptation dataset exactly as specified in the passage; (2) Insert LoRA adapters into the stated projection/MLP modules; (3) Train for the stated number of epochs/steps with the stated batch size and learning rate; (4) Enforce any stated cost/step budget; (5) Evaluate on the stated held-out tasks and record success rate; (6) Keep only configurations that meet the reward/cost criterion for further optimization.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 9
|
passage comes bundled with reference QA. This coupling simplifies reward computation but prevents RL training of SEAL from scaling to unlabeled corpora. A potential solution is to let the model generate not only self-edits but also its own evaluation questions—e.g., draft QA items or synthetic
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88
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 10
|
self-evolving embodied agents. To address the challenges of sparse rewards and limited generaliza- tion for embodied domains, SEEA-R1 integrates Tree-GRPO, which leverages MCTS to densify reward signals, and MGRM, a multi-modal reward model that generalizes across tasks and environ-
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89
|
What dataset split/evaluation protocol is specified here, and how would you implement it to avoid contamination?
|
Implement the split exactly as described: keep training and evaluation sets disjoint, and evaluate on the specified split (e.g., test-unseen / held-out subset). Add a leakage check by hashing identifiers (titles/contexts) across splits and reject any overlaps. Report metrics on the specified evaluation split only, and log the split manifest for reproducibility.
|
SEAL (arXiv:2506.10943)
|
2506.10943.pdf
| 18
|
model. Since this model has no specialized training on ARC, its ability to solve ARC tasks is limited. To enable controlled evaluation, we curated a small set of ARC problems from the training and evaluation splits that are solvable with optimal TTT hyperparameters.
|
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90
|
From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 10
|
tion for embodied domains, SEEA-R1 integrates Tree-GRPO, which leverages MCTS to densify reward signals, and MGRM, a multi-modal reward model that generalizes across tasks and environ- ments. SEEA-R1 achieves new state-of-the-art performance on ALFWorld, outperforming previous
|
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91
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
|
Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 3
|
research and applications in the embodied intelligence community, we will open-source our full framework and modular components—including our reward model MGRM, and training pipelines. 2 Related Works
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92
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
|
Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 22
|
enhanced robustness in reinforcement learning. G.4 Long-Term Performance of Tree-GRPO To thoroughly assess the long-term effectiveness of Tree-GRPO, we extended the training duration
|
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93
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
|
Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup. Parameters found in the passage: based=21.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 21
|
of MCTS+SFT clearly reflect the inherent limitations of purely supervised learning in complex interactive environments. The on-policy nature of GRPO, especially when combined with tree-based 21
|
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94
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
|
Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
|
SEEA-R1 (arXiv:2506.21669v2)
|
2506.21669v2.pdf
| 20
|
G.2 Impact of Sample Size and Batch Size on Training Stability We investigate how training configurations affect model performance. As illustrated in Figure 9, larger sample and batch sizes significantly influence the final success rate on the ALFWorld unseen
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95
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
| 2
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framework to adopt RFT for training embodied agents capable of long-horizon reasoning and autonomous self-evolution. SEEA-R1 integrates two key components. 1)Tree-GRPO(Tree-based Group Relative Policy Optimization): we extend Group Relative Policy Optimization with Monte
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96
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From this passage, what are the implementable steps of the MCTS loop, and what values should be logged at each step?
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Implement an MCTS loop with four phases: (1) Selection using a UCT-style score to traverse the tree, (2) Expansion by adding a new node and enumerating candidate actions, (3) Simulation/rollout to estimate returns, and (4) Backup to update visit counts and Q-value estimates along the path. Log per edge: N(s,a), Q(s,a), chosen action, depth, termination reason, and any reward/score used for backup.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
| 5
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precision of these Q-values and therefore the quality of the rewards of the process improves as MCTS performs more simulations (Figure 3) exploring a wider range of trajectories. This progressive refinement of MCTS-generated experiential data is how SEEA-R1implements’Data Evolution’,
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97
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
| 20
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applications. G.2 Impact of Sample Size and Batch Size on Training Stability We investigate how training configurations affect model performance. As illustrated in Figure 9,
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98
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
| 29
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are standardized to minutes for consistency. Table 10: Training Efficiency Comparison of SEEA-R1 Under Different Reward Configurations (Per Training Iteration).
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99
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Summarize the compute/training-cost constraints described here and convert them into enforceable engineering limits.
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Convert the stated compute constraints into hard caps in code: maximum steps per run, maximum wall time, and maximum sampled candidates. Log GPU type/count, tokens/steps, sampling time vs training time, and enforce early termination when the stated limits are exceeded. Track median cost per successful run alongside accuracy to prevent trading quality for uncontrolled compute. Parameters found in the passage: with=8.
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SEEA-R1 (arXiv:2506.21669v2)
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2506.21669v2.pdf
| 28
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All experiments were conducted on a high-performance computing cluster equipped with 8 NVIDIA A100 80GB GPUs. We used the MS-Swift framework for distributed model training, which provided efficient scaling across multiple GPUs. For inference performance evaluation, we employed the
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100
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Turn the procedure described in this passage into an implementable training loop (steps, artifacts, and metrics).
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Implementation loop: (1) generate training candidates as described (sampling policy/self-edits/trajectories); (2) evaluate each candidate under the stated protocol; (3) assign reward/labels; (4) filter or select winners; (5) update the model with the stated optimizer/adapter method; (6) checkpoint and run evaluation; (7) repeat for the stated number of rounds/iterations. Artifacts: datasets/buffers, config, checkpoints, eval reports; Metrics: success rate, cost, stability across seeds. Parameters found in the passage: selected=11; selected=8; These=8.
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SEAL (arXiv:2506.10943)
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2506.10943.pdf
| 18
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Training Set:We selected 11 ARC tasks from the training set as the environment for RL optimization. Evaluation Set:We selected 8 distinct ARC problems from the evaluation set for measuring generalization performance. These 8 were explicitly filtered for being amenable to TTT out of the
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