Datasets:
task stringclasses 1
value | env_id stringclasses 1
value | env_kwargs dict | seed int64 2.03B 2.03B | episode_seed int64 3.03B 3.03B | episode int64 0 33.6k | extra_state dict | init_state dict | init_state_hash stringlengths 64 64 | stats dict | version stringclasses 1
value | messages listlengths 6 14 | chunk_size int64 3 3 | n_steps int64 9 20 | n_chunks int64 3 7 | n_imagined_frames int64 0 0 | n_feedback_turns int64 2 6 | n_feedback_frames int64 2 6 | max_steps int64 20 20 | schema_version stringclasses 1
value | cot_row_type stringclasses 1
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pacman_2d_easy_oracle_success_v0 | pacman_2d/easy | {
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pacman_2d_easy_oracle_success_v0 | pacman_2d/easy | {"difficulty":"easy","grid_size":9,"food_count":8,"wall_density":0.22,"max_steps":20,"horizon":8,"ti(...TRUNCATED) | 2,026,080,200 | 3,026,080,262 | 5 | {"render_size":256,"jpeg_quality":95,"solve_info":{"strategy_requested":"receding_horizon","planner_(...TRUNCATED) | {"backend":"custom_grid_pacman_python_assets","difficulty":"easy","grid_size":9,"food_count":8,"wall(...TRUNCATED) | 17183bf68e5cbdcaa129070828b783a6223fb5dc732faa0f272059ae4ed7d241 | {"step":10,"reward":1.0,"terminated":true,"truncated":false,"success":true,"won":true,"food_remainin(...TRUNCATED) | visgym_pacman_oracle_success_easy_v1 | [{"role":"user","content":[{"type":"text","text":"You are Pacman in a deterministic 9x9 maze. Collec(...TRUNCATED) | 3 | 10 | 4 | 0 | 3 | 3 | 20 | pacman_noncot_actiononly_envfb_k3_v1 | actiononly_multiturn_gtenvfeedback |
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pacman_2d_easy_oracle_success_v0 | pacman_2d/easy | {"difficulty":"easy","grid_size":9,"food_count":8,"wall_density":0.22,"max_steps":20,"horizon":8,"ti(...TRUNCATED) | 2,026,080,200 | 3,026,080,200 | 9 | {"render_size":256,"jpeg_quality":95,"solve_info":{"strategy_requested":"receding_horizon","planner_(...TRUNCATED) | {"backend":"custom_grid_pacman_python_assets","difficulty":"easy","grid_size":9,"food_count":8,"wall(...TRUNCATED) | 6fdc25b09d38b4f66a851812d898ff92d34914a3701976e69c2f291585884656 | {"step":10,"reward":1.0,"terminated":true,"truncated":false,"success":true,"won":true,"food_remainin(...TRUNCATED) | visgym_pacman_oracle_success_easy_v1 | [{"role":"user","content":[{"type":"text","text":"You are Pacman in a deterministic 9x9 maze. Collec(...TRUNCATED) | 3 | 10 | 4 | 0 | 3 | 3 | 20 | pacman_noncot_actiononly_envfb_k3_v1 | actiononly_multiturn_gtenvfeedback |
End of preview. Expand in Data Studio
pacman_easy_noncot_chunk_k3_train
BAGEL VLM-Gym world-model dataset (pacman / noncot).
- Non-CoT chunk-K train set (no imagined reasoning); re-grounds every K=3 steps.
- layout: Train-only. Gzipped-JSONL shards under
training/; each row is one packed SFT sample with base64-JPEG frames inline. - images are base64-encoded JPEG frames stored inline in each JSONL row.
Pairs with the matching pacman checkpoint(s) under the companion model org; CoT and non-CoT
variants share the same underlying rollouts.
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