{"episode": 0, "episode_seed": 1608637542, "split": "train", "timestamp": "2025-09-15T18:42:13.188703", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1608637542, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([1, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([1, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([2, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([8, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "58e5509d425f50d8bed8b099592fd5aaf4b8b953500d16f7136a6e4e1900d7a1"}
{"episode": 1, "episode_seed": 3421126067, "split": "train", "timestamp": "2025-09-15T18:42:13.290631", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3421126067, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([3, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([3, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([7, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "95e1ef5267b6aff687c5b5a95940a27cfb9acd495307c012c7e6161e1aeff84e"}
{"episode": 2, "episode_seed": 4083286876, "split": "train", "timestamp": "2025-09-15T18:42:13.391902", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4083286876, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([1, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([1, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([1, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([1, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([2, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([6, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "7d7076f2a6288e710e5bcf22255d798004e233982fbda9581b8f7910e5185d39"}
{"episode": 3, "episode_seed": 787846414, "split": "train", "timestamp": "2025-09-15T18:42:13.492898", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 787846414, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([2, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([8, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([9, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([9, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "fb7dab22da246f20c3f62ce04e8203f6221637576c45abab780e969ab7eae685"}
{"episode": 4, "episode_seed": 3143890026, "split": "train", "timestamp": "2025-09-15T18:42:13.640978", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3143890026, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 25, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([2, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([1, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([1, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "7087b260b08af3aa99c802c71e66a83700071df65f4e4cc35921adb6e85f104c"}
{"episode": 5, "episode_seed": 3348747335, "split": "train", "timestamp": "2025-09-15T18:42:13.742656", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3348747335, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([4, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([7, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([8, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "36f8e66d8373e00c21841863fbf4e5f804f6c36300c5e31419ae6b57bf0a6817"}
{"episode": 6, "episode_seed": 2571218620, "split": "train", "timestamp": "2025-09-15T18:42:13.844942", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2571218620, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "ce51a24b71f3438542defec3bc815607656bbbfd4015d59cf2c305d2672b3abb"}
{"episode": 7, "episode_seed": 2563451924, "split": "train", "timestamp": "2025-09-15T18:42:13.970177", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2563451924, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([3, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "b168d741079ef87d0d6dfd0a1ae100af521e4ae95cfce20e6a8cc733c19fe3d9"}
{"episode": 8, "episode_seed": 670094950, "split": "train", "timestamp": "2025-09-15T18:42:14.071591", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 670094950, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([3, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "7a595a70f1cde10ca4f0e804bbf68ecc3feddb7fffb443e325856047f45e953e"}
{"episode": 9, "episode_seed": 1914837113, "split": "train", "timestamp": "2025-09-15T18:42:14.172594", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1914837113, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([2, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([1, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([1, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "2b2051991087c4c707b607b30e7319f8ec4a1291ded6b515e8e296fd9ff6fc72"}
{"episode": 10, "episode_seed": 669991378, "split": "train", "timestamp": "2025-09-15T18:42:14.273153", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 669991378, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "ea6e8bd4b617c148ef15915e574a3d16441079e8ab3c60d3e2ea3cbc47ef329b"}
{"episode": 11, "episode_seed": 429389014, "split": "train", "timestamp": "2025-09-15T18:42:14.420458", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 429389014, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 25, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"image_prev": "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", "image_next": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": "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", "image_prev": "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", 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 13.0, 'agent': array([8, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([3, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "5d3bdd7cf29ebfe83af448ccf0352782d7ac8cec321c95544a93f0c794f2dda3"}
{"episode": 12, "episode_seed": 249467210, "split": "train", "timestamp": "2025-09-15T18:42:14.568108", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 249467210, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 25, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([3, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([2, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([1, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([1, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "2befebf4775ac4da34d2c8b0e4dc83c8be559fec5542ed7f151ee385c377181e"}
{"episode": 13, "episode_seed": 1972458954, "split": "train", "timestamp": "2025-09-15T18:42:14.738103", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1972458954, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 29, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([1, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([1, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([2, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([3, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([3, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([4, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([8, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([9, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([9, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([9, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([8, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([8, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([9, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([9, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 25, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 26, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 27, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 28, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "80901aa820a29fe02f0281a24f8be7903031dfd97430bca11b7fafbe4cc7dff7"}
{"episode": 14, "episode_seed": 3720198231, "split": "train", "timestamp": "2025-09-15T18:42:14.884820", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3720198231, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 25, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([4, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([8, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([9, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([9, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([9, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([8, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([7, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([6, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "dc977baec64cc34068d05872105fad48352ebc9c53e11364e8a18ee7b3567255"}
{"episode": 15, "episode_seed": 1433267572, "split": "train", "timestamp": "2025-09-15T18:42:15.008568", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1433267572, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "bf3890685669866fa2e1c63df9dc01babe41d953551f69f4ab3c19ef0e97626f"}
{"episode": 16, "episode_seed": 2581769315, "split": "train", "timestamp": "2025-09-15T18:42:15.108248", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2581769315, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([2, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([3, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([3, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([6, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "db9df3f40bbd24c992c784bada8ceab23e906d09b22ce115d7b87a1ce5e70c11"}
{"episode": 17, "episode_seed": 613608295, "split": "train", "timestamp": "2025-09-15T18:42:15.208527", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 613608295, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"image_prev": 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"image_next": 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"info": "{'distance': 15.0, 'agent': array([2, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"image_prev": "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", "image_next": 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"info": "{'distance': 14.0, 'agent': array([3, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([4, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([7, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([8, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "4a2f7766102d6c684997f0b17dd3d9efa7857f220e4be6dcf430850d2cce2abd"}
{"episode": 18, "episode_seed": 3041148567, "split": "train", "timestamp": "2025-09-15T18:42:15.307673", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3041148567, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([3, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "ba984ab940bc5e11f4af34411d8112c7141ce7889a8c81e7e7148e7161759479"}
{"episode": 19, "episode_seed": 2795544706, "split": "train", "timestamp": "2025-09-15T18:42:15.407099", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2795544706, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "30b4c69b1c33a1127162854eb6524c82f8f43136d24e846bd154f345060ec30b"}
{"episode": 20, "episode_seed": 88409749, "split": "train", "timestamp": "2025-09-15T18:42:15.504261", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 88409749, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([3, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([8, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([9, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([9, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([9, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([9, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "bf0d0371a3f5ab6943f9c4539d6672b777bcd2dcaea9db1c214ec3699c689b07"}
{"episode": 21, "episode_seed": 242285876, "split": "train", "timestamp": "2025-09-15T18:42:15.602011", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 242285876, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([3, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([3, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "b2003cd4a860484126a2a41d031bc2cd71b4dcec0274c533b82331686b294eb1"}
{"episode": 22, "episode_seed": 4165731073, "split": "train", "timestamp": "2025-09-15T18:42:15.699368", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4165731073, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([9, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([9, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([8, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "5cfd40d8e2ef82a4d26059b0196d884bbd3c0d38793b3a48360ced5aae849d4f"}
{"episode": 23, "episode_seed": 3100961111, "split": "train", "timestamp": "2025-09-15T18:42:15.820244", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3100961111, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([1, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([1, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([1, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([1, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([1, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([1, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([2, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([3, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([6, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "fb60067159326bb1a61a2cf49aeb9980d017d04508d4bb1a5f74f22ad8638c3e"}
{"episode": 24, "episode_seed": 3575313899, "split": "train", "timestamp": "2025-09-15T18:42:15.944818", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3575313899, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([2, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([8, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "686fdad1ae85281f1d06dfa8132834a1788e127ce7c2bc1c401ee3e0aff4ca97"}
{"episode": 25, "episode_seed": 4031053213, "split": "train", "timestamp": "2025-09-15T18:42:16.042338", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4031053213, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([4, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([7, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([8, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "5c07fb69d638995f5bc6359744ec169d8c882a39af22cba6fa17ac6516daf2f0"}
{"episode": 26, "episode_seed": 911989541, "split": "train", "timestamp": "2025-09-15T18:42:16.161967", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 911989541, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"image_prev": 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"image_next": 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"info": "{'distance': 15.0, 'agent': array([8, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 14.0, 'agent': array([7, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "57744d2eecc1d6fe4132d6dfeacbbbd5e5323edbc0d80799d03fbddb20f5ee0b"}
{"episode": 27, "episode_seed": 3344769, "split": "train", "timestamp": "2025-09-15T18:42:16.261497", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3344769, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([3, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "3e447e48ce0d53b5377967471fd3dc94edbb51f78d7d13e0efaa8e29d730b76a"}
{"episode": 28, "episode_seed": 780932287, "split": "train", "timestamp": "2025-09-15T18:42:16.360986", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 780932287, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([2, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([3, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([8, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "92ddf2c51873a75f549f0ba4f62ed5b4d292ec1c223b3d13a167e755447ab9e4"}
{"episode": 29, "episode_seed": 4261516219, "split": "train", "timestamp": "2025-09-15T18:42:16.484633", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4261516219, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([9, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([9, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([8, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "dc782d7b137432b8c87bf8a32692a29fa1d80965235535a151387ffd4977c42f"}
{"episode": 30, "episode_seed": 787716372, "split": "train", "timestamp": "2025-09-15T18:42:16.585116", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 787716372, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([4, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([8, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([9, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([9, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "ddd69ec8b30e88e7280cbcc7c497d073661a2fc54d2791ac1acea0be288ae36a"}
{"episode": 31, "episode_seed": 2652062880, "split": "train", "timestamp": "2025-09-15T18:42:16.687256", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2652062880, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([3, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "f2cc84012de882b95cd8906c067efd3a1fdd9615ddf913826f9f3b72b18bd05f"}
{"episode": 32, "episode_seed": 1306710475, "split": "train", "timestamp": "2025-09-15T18:42:16.789614", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1306710475, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([1, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([1, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([2, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([7, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "fbc1a6463096ad848712175567c8bda0f7c54410c6538bb6a698243443f48ea4"}
{"episode": 33, "episode_seed": 2627030329, "split": "train", "timestamp": "2025-09-15T18:42:16.938582", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2627030329, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 25, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([4, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([8, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([9, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([9, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([9, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([8, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([6, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "8651041ea0e31a8f5a14b91515e541eecca84cfae89021a54b5b71de1a9a01ea"}
{"episode": 34, "episode_seed": 2253811733, "split": "train", "timestamp": "2025-09-15T18:42:17.037043", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2253811733, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([2, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([3, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([7, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "f4c6f849f4187f192ab03af8b7ec1348f47d2fe03f8ce6a7865a8e945f465893"}
{"episode": 35, "episode_seed": 30349564, "split": "train", "timestamp": "2025-09-15T18:42:17.134784", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 30349564, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 15.0, 'agent': array([2, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 14.0, 'agent': array([3, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([4, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([7, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([8, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "c8eaedbcea77f83b2c85a58174ad1afa83704c59fe080381728fd0795c0975e6"}
{"episode": 36, "episode_seed": 1855189739, "split": "train", "timestamp": "2025-09-15T18:42:17.255442", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1855189739, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([7, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "ef68c7742bceccc8797fb93cd52ee06ad9ed6e31275597dfdd3dc1431e12c33f"}
{"episode": 37, "episode_seed": 99052376, "split": "train", "timestamp": "2025-09-15T18:42:17.352624", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 99052376, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([7, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "af7ba91f94836ed37835f491a040b4db312cc7f00e2d6e21d369157d72d0c08e"}
{"episode": 38, "episode_seed": 1250819632, "split": "train", "timestamp": "2025-09-15T18:42:17.516208", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1250819632, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 29, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([2, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([1, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([1, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([1, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([1, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 25, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 26, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 27, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 28, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "2a0a380efd3d7c72f36c70eb40afec01128b26dab9d852a34630d42d9891866a"}
{"episode": 39, "episode_seed": 2253890010, "split": "train", "timestamp": "2025-09-15T18:42:17.611796", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2253890010, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([9, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([9, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([8, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "f8ab9addb46d94731d85c142d699f8317fa55e78f40965b9f57b648f105758db"}
{"episode": 40, "episode_seed": 2627888186, "split": "train", "timestamp": "2025-09-15T18:42:17.708453", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2627888186, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "b985a3f9ac72f0ef2b619a8c4ef81c52bda87f7bfb18a02f6c83e96fc3a75009"}
{"episode": 41, "episode_seed": 1717389822, "split": "train", "timestamp": "2025-09-15T18:42:17.827570", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1717389822, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([2, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([1, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([1, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([1, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([2, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([3, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([3, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "2a828d4d789df3bd15f7b86292d3bcd48247040acfcfd991338e5b26be8dfdf2"}
{"episode": 42, "episode_seed": 599121577, "split": "train", "timestamp": "2025-09-15T18:42:17.947259", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 599121577, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([3, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([3, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([2, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([1, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([1, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([1, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([2, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([6, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "fe2942f8b35e7eef403194a9b1dd6f815ee729e4e01e560c69d57989c4c00119"}
{"episode": 43, "episode_seed": 200427519, "split": "train", "timestamp": "2025-09-15T18:42:18.042909", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 200427519, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([9, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([9, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([8, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "9e133004957edd751ce0ca383af095522aa32267c781ff37020bb3d71d6af180"}
{"episode": 44, "episode_seed": 1254751707, "split": "train", "timestamp": "2025-09-15T18:42:18.138280", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1254751707, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "7646085d1602e54eae4abc426cff1b7925bc568c716152f81aea103923c1b50e"}
{"episode": 45, "episode_seed": 4182248123, "split": "train", "timestamp": "2025-09-15T18:42:18.233529", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4182248123, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([9, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([9, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([8, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "199bd502441682ab28fdeddf4ab9c7f457177dbd2d16f78b1d644669335fcd05"}
{"episode": 46, "episode_seed": 1573512143, "split": "train", "timestamp": "2025-09-15T18:42:18.352305", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1573512143, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([7, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([9, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([9, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([9, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([9, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([8, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "71d210b047802671aebca57baa45e0481bce557b8e1d74d4929cfe52893771b3"}
{"episode": 47, "episode_seed": 999745294, "split": "train", "timestamp": "2025-09-15T18:42:18.448958", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 999745294, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([7, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([3, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "59cc1470d2608444beee9a67a7ccbc69f3ea08503bcc88da6a847b6c7b5c314d"}
{"episode": 48, "episode_seed": 1958805693, "split": "train", "timestamp": "2025-09-15T18:42:18.570301", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1958805693, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([2, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([1, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([1, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "0bf2473bd757dcbe995fabbc75e4e50245d65984de03d76a3546e05a2d246f61"}
{"episode": 49, "episode_seed": 389151677, "split": "train", "timestamp": "2025-09-15T18:42:18.689300", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 389151677, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([1, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([1, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([2, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([3, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([8, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "9b5bdfe278a69ce266e0d986c251c7bba274a615953a48dc0850589fb398df58"}
{"episode": 50, "episode_seed": 3372305070, "split": "train", "timestamp": "2025-09-15T18:42:18.788322", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3372305070, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([1, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([1, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([1, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([1, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([1, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([1, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([2, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([6, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "73222f3c684ae1bc40005e90266eb7d52c61da4851126ae5923b208434802858"}
{"episode": 51, "episode_seed": 2655947709, "split": "train", "timestamp": "2025-09-15T18:42:18.910643", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2655947709, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 15.0, 'agent': array([9, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 14.0, 'agent': array([9, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([8, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([9, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([9, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([9, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([8, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "96e6e93de3db23662b6e29c6ca71194fc019d024e72e353ba07bbdefa0b0a0f1"}
{"episode": 52, "episode_seed": 857592370, "split": "train", "timestamp": "2025-09-15T18:42:19.034853", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 857592370, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([3, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "252d0bf175e8cdc07a9f7b74e9e676d92a4ef89f55054142eee79c4052b2e33a"}
{"episode": 53, "episode_seed": 1642661739, "split": "train", "timestamp": "2025-09-15T18:42:19.135706", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1642661739, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "d2c3097744f05db350c2b6d72926458acb08ebf54daa478bc59d76677c277d58"}
{"episode": 54, "episode_seed": 2208620086, "split": "train", "timestamp": "2025-09-15T18:42:19.260391", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2208620086, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([4, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([3, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([7, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "01512340c402fc67e94ea65ca49c8b473678af24168fd4dd2ace773bf83074d0"}
{"episode": 55, "episode_seed": 4222944499, "split": "train", "timestamp": "2025-09-15T18:42:19.383685", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4222944499, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([9, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([9, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([9, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([9, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([8, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "1f34aa9bfbae8bd5c65f15933f0eb912ee98433c19db7c0e2545350fbbc43e54"}
{"episode": 56, "episode_seed": 2544401215, "split": "train", "timestamp": "2025-09-15T18:42:19.482877", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2544401215, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([2, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([1, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([1, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([1, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([1, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "2e54dec306e918a4560aad19df237ba7ad4722fd96dac82c1450ee845d643eb8"}
{"episode": 57, "episode_seed": 2004731384, "split": "train", "timestamp": "2025-09-15T18:42:19.581992", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2004731384, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([2, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([3, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([8, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "57f4a8c1f75206676b1da6c2e199e501327b4be3c21064226e81c41344d22693"}
{"episode": 58, "episode_seed": 199502978, "split": "train", "timestamp": "2025-09-15T18:42:19.680952", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 199502978, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"image_prev": 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"image_next": 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"info": "{'distance': 15.0, 'agent': array([1, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"image_prev": "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", "image_next": 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"info": "{'distance': 14.0, 'agent': array([1, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 13.0, 'agent': array([2, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([6, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "79d0f9ad7fdceddd68c6b03138691cce36214538493f03b2dee5895aeaf0eb20"}
{"episode": 59, "episode_seed": 3693415908, "split": "train", "timestamp": "2025-09-15T18:42:19.779593", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3693415908, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"image_prev": 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"image_next": 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"info": "{'distance': 15.0, 'agent': array([2, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"image_prev": "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", 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"info": "{'distance': 14.0, 'agent': array([3, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([3, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([3, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([7, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "8c30b2766b2dd6b54f0fcab4a72368600e87f041f66d66d12de58570586198be"}
{"episode": 60, "episode_seed": 2609385266, "split": "train", "timestamp": "2025-09-15T18:42:19.880252", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2609385266, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([3, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([7, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "7ed8d7bf22a37fa3d04b1e36a8fd22a509009a31bc0f1fd2738149693edd8a34"}
{"episode": 61, "episode_seed": 2921898630, "split": "train", "timestamp": "2025-09-15T18:42:20.000873", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2921898630, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([3, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([7, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([7, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "07e87db44fc7606f4da62d07128be2d827d2a20675322a218c0afd0d7b6fb7f8"}
{"episode": 62, "episode_seed": 732395540, "split": "train", "timestamp": "2025-09-15T18:42:20.099281", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 732395540, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([1, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([1, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([2, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([7, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "b46e698ec6d28138bd691a7f27e4d62157123eaf69fd59a03876a1f3537b94b0"}
{"episode": 63, "episode_seed": 1934879560, "split": "train", "timestamp": "2025-09-15T18:42:20.219243", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1934879560, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([3, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([3, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([2, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([1, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([1, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([1, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([2, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([6, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "504cfd63c7312f9453b78b151cd3cd8e7e374becfbe3ced79f2df73fc0eb3335"}
{"episode": 64, "episode_seed": 279394470, "split": "train", "timestamp": "2025-09-15T18:42:20.317159", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 279394470, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "2cfa078b40810f7c8564f90260f1564a62c52c55a4b80db18460de151d6efcff"}
{"episode": 65, "episode_seed": 56972561, "split": "train", "timestamp": "2025-09-15T18:42:20.415046", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 56972561, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([7, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "c4a91742677a05a24ea8c5ec43af3e652436a7b731fe5bc7f077d57ad114c9ff"}
{"episode": 66, "episode_seed": 4075432323, "split": "train", "timestamp": "2025-09-15T18:42:20.514973", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4075432323, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([7, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "797e41c07d478306a2ba0e37899099faff753fb82cc1c6b458a2acd50e2bd4cf"}
{"episode": 67, "episode_seed": 4046725720, "split": "train", "timestamp": "2025-09-15T18:42:20.638675", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4046725720, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([4, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([8, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([9, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([9, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "239b92a23f1011f22e648d149f28ad0e57abf3eeda17807a989c2dc9712be7c4"}
{"episode": 68, "episode_seed": 4147358011, "split": "train", "timestamp": "2025-09-15T18:42:20.762687", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4147358011, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([4, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([7, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([7, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "432dd107637cc0b3b1e914a634236763b9a2743b8b0ea15b2320c23b4be3c815"}
{"episode": 69, "episode_seed": 2419304461, "split": "train", "timestamp": "2025-09-15T18:42:20.864957", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2419304461, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([9, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([9, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([8, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "9c53dcbca18ec16a8c7d53e2ac5e9bef0858860ac07feaf28c2c9bccdfd4c2ac"}
{"episode": 70, "episode_seed": 3472040177, "split": "train", "timestamp": "2025-09-15T18:42:20.967618", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3472040177, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([7, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "ef087694b7d18d450c76018b5ceb180c74ed6a704f9bc5a385abd7d483ec9337"}
{"episode": 71, "episode_seed": 1655351289, "split": "train", "timestamp": "2025-09-15T18:42:21.070040", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1655351289, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "26b0ff7878d4efd1a1966cf97b04d94aeb400b537984ef9a6aa1449c38fe4fab"}
{"episode": 72, "episode_seed": 1308306184, "split": "train", "timestamp": "2025-09-15T18:42:21.220978", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1308306184, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 25, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "b0ead55b91239f54a496a0dfc08c9b7bb22fb10e9810cc2f3534fab0e9ea0d51"}
{"episode": 73, "episode_seed": 68574553, "split": "train", "timestamp": "2025-09-15T18:42:21.344006", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 68574553, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([7, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([2, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([1, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([1, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "4edcbadce10685de761417a3a7dc72f65c07b2792e608f28405c8b7fc5ba6d5c"}
{"episode": 74, "episode_seed": 419498548, "split": "train", "timestamp": "2025-09-15T18:42:21.442553", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 419498548, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([2, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([1, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([1, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "835a415843ad0fc2d57235116fd8a9ec6cda5dab512d47a15b2b0f9b757422c4"}
{"episode": 75, "episode_seed": 991681409, "split": "train", "timestamp": "2025-09-15T18:42:21.563116", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 991681409, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "296b474a06245e691e278d7dd4ead06491dd675bb63ed991c566dd0a09a5cb64"}
{"episode": 76, "episode_seed": 2938758483, "split": "train", "timestamp": "2025-09-15T18:42:21.683211", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2938758483, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "8bb5380db990897fd88d6ab01d27df9d96632c19dc3e2e8a453e2de5cf5ca4a1"}
{"episode": 77, "episode_seed": 1035196507, "split": "train", "timestamp": "2025-09-15T18:42:21.822667", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1035196507, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 25, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([9, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([9, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([9, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([9, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([8, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([3, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "e8b6d4a0ca9d90a071d61225501bd88f47b5ed4bfaa83513916419f00338440b"}
{"episode": 78, "episode_seed": 1890440558, "split": "train", "timestamp": "2025-09-15T18:42:21.966675", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1890440558, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 25, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([2, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([1, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([1, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([1, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([2, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([3, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "89b72fe5e9d28e21bc310e4be6f97d12a928571e1a34f35a3c754b4eb9b4a8eb"}
{"episode": 79, "episode_seed": 2934594491, "split": "train", "timestamp": "2025-09-15T18:42:22.089155", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2934594491, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([7, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "a45ea0b76b0ea4e82c6ebcb42c2a3f42464271e07aa05ea4853e1e836ca405be"}
{"episode": 80, "episode_seed": 524150214, "split": "train", "timestamp": "2025-09-15T18:42:22.255323", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 524150214, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 29, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([7, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 25, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 26, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 27, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 28, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "865869964e1655a641b71480b15857e8c9b575516808df0d8ffd18e2f3e301da"}
{"episode": 81, "episode_seed": 2619915691, "split": "train", "timestamp": "2025-09-15T18:42:22.351869", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2619915691, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([1, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([1, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([1, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([1, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([1, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([1, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([2, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([6, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "2d19fc784625b9a2b8c7ef167b1b4706617493f0a4214263cc99e651ca29b529"}
{"episode": 82, "episode_seed": 2126768636, "split": "train", "timestamp": "2025-09-15T18:42:22.448941", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2126768636, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"image_prev": "iVBORw0KGgoAAAANSUhEUgAAARMAAAETCAIAAACay3XvAAADwUlEQVR4nO3cwWrbQBRAUbvkG5VvlH7SXTa0IXFuB2nGnLOyN8Mg5/IgNu++7/sN+KFfV18AlqQcKJQDhXKgUA4UyoFCOVAoBwrlQKEcKJQDhXKgUA4UyoFCOVAoBwrlQKEcKJQDhXKgUA4UyoFCOVAoB4q3gWdt7++jjjrG7U/ctm3UUXM6jmPUUZ7V88wcKJQDhXKgUA4UyoFCOVAoBwrlQKEcKJQDhXKgUA4UyoFCOVAoBwrlQKEcKJQDhXKgUA4UyoFCOVAoBwrlQDFyU+Ht8Rh21LiNcgOX0w1kJ+Dz5nxWZg4UyoFCOVAoBwrlQKEcKJQDhXKgUA4UyoFCOVAoBwrlQKEcKJQDhXKgUA4UyoFCOVAoBwrlQKEcKJQDhXKgGLqpcEoD99zNufSQS5g5UCgHCuVAoRwolAOFcqBQDhTKgUI5UCgHCuVAoRwolAOFcqBQDhTKgUI5UCgHCuVAoRwolAOFcqBQDhTKgUI5UCgHCuVAoRwolAOFcqBQDhTKgUI5UCgHCuVAoRwolAOFcqBQDhTKgUI5UCgHCuVAoRwolAOFcqBQDhTKgUI5ULxdfYGVbNt29RU+MeetXp6ZA4VyoFAOFMqBQjlQKAcK5UChHCiUA4VyoFAOFMqBQjlQKAcK5UChHCiUA4VyoFAOFMqBQjlQKAcK5UChHChef1PhcRxXX2EZlh4+z8yBQjlQKAcK5UChHCiUA4VyoFAOFMqBQjlQKAcK5UChHCiUA4VyoFAOFMqBQjlQKAcK5UChHCiUA4VyoFAOFJNuKrQy73lWMV7CzIFCOVAoBwrlQKEcKJQDhXKgUA4UyoFCOVAoBwrlQKEcKJQDhXKgUA4UyoFCOVAoBwrlQKEcKJQDhXKgUA4Uk24qfPnte1YxPm/OPwYzBwrlQKEcKJQDhXKgUA4UyoFCOVAoBwrlQKEcKJQDhXKgUA4UyoFCOVAoBwrlQKEcKJQDhXKgUA4UyoFCOVBMuqlwzkV+c67Mm/NZzXmrgZ+gmQOFcqBQDhTKgUI5UEz6vzVe2/3+5/Xjcd09/oOZw9k+ZvPv21Uoh1N92smK8SiH83xRyHLxKIeTfNvGWvEoBwrlQKEcKJTDSb793matL3aUw3m+aGOtbG7K4WSfFrJcNjflcL6/Olkxm5vfrXGJRWv5yMyBQjlQKAcK5UChHCiUA8V93/er7wDrMXOgUA4UyoFCOVAoBwrlQKEcKJQDhXKgUA4UyoFCOVAoBwrlQKEcKJQDhXKgUA4UyoFCOVAoBwrlQKEcKJQDxW+UaEOD3I1WuQAAAABJRU5ErkJggg==", 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([6, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "8e0dfb74a1fb83d105c10b6b73c02a0c0e4bcb3a648ec6470454dd9d9d6ce105"}
{"episode": 83, "episode_seed": 3578544903, "split": "train", "timestamp": "2025-09-15T18:42:22.569131", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3578544903, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([4, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([6, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "4bd385b4f18000b330ba49f81afbada552da66ce3045d82bd4e061e04194fc10"}
{"episode": 84, "episode_seed": 147697582, "split": "train", "timestamp": "2025-09-15T18:42:22.713781", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 147697582, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 25, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([7, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([9, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([9, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([8, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "42ca2115aaaeb06937d00cb000ac4eb4bd47c5629b8feff8ecddd57ec41966e2"}
{"episode": 85, "episode_seed": 744595490, "split": "train", "timestamp": "2025-09-15T18:42:22.812439", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 744595490, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([2, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([1, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([1, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([1, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([1, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "d20c235de4bf34bb72dcc4cc8b75af3353d06cf6bb7b0f3c4edbd168cf03628f"}
{"episode": 86, "episode_seed": 3905501389, "split": "train", "timestamp": "2025-09-15T18:42:22.911841", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3905501389, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([3, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([3, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([3, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([6, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "fa00dc0e69b25467a076ad91caff2f7fafa63707d3dff6688e32fbd1ce8fee83"}
{"episode": 87, "episode_seed": 1679592528, "split": "train", "timestamp": "2025-09-15T18:42:23.011146", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1679592528, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([1, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([1, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([2, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([6, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "cf88c640528f8d933eb20ed87db36025ce92a34b61f578f72b7d1826da928772"}
{"episode": 88, "episode_seed": 1111451555, "split": "train", "timestamp": "2025-09-15T18:42:23.109241", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1111451555, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([7, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([4, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "e6e02020245961dd75378625b33ce35b9e75b7554b80be4184528cde253a3eff"}
{"episode": 89, "episode_seed": 782698033, "split": "train", "timestamp": "2025-09-15T18:42:23.275564", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 782698033, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 29, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([5, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([3, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 25, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 26, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 27, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 28, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "31db333cb7626fa894c5322d221dfe072c5092525979254c8ac3a36636503ed7"}
{"episode": 90, "episode_seed": 2845511527, "split": "train", "timestamp": "2025-09-15T18:42:23.394487", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2845511527, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([9, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([9, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([9, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([9, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([8, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([7, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "41175cc52f9350783a0dd85bcb0e9842e1a54b9892d98e88a15c7fd1c49d7ed1"}
{"episode": 91, "episode_seed": 3244252547, "split": "train", "timestamp": "2025-09-15T18:42:23.534668", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3244252547, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 25, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([8, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([7, 9]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([7, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([3, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([3, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([2, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 21, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 22, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 23, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 24, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "5bf57a1e894d5d8f958bde357f1fbb3c9a240822b2e9e55ddf72ca1c57f07d2e"}
{"episode": 92, "episode_seed": 1338788865, "split": "train", "timestamp": "2025-09-15T18:42:23.628201", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1338788865, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([9, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([9, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([8, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([7, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([7, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([5, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([5, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([4, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([3, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([2, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "c6bbf529a0a93d350a70e8f798629242ebd5416dea4e1b34056c575c8231cf44"}
{"episode": 93, "episode_seed": 1826030589, "split": "train", "timestamp": "2025-09-15T18:42:23.743483", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1826030589, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([2, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([8, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([9, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([9, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([9, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([8, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([7, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([7, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([7, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([8, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "c7c58aa306ef28a5659b876303d3c3e77b0711d986c32d549e69d812bd0f8f8c"}
{"episode": 94, "episode_seed": 2233675141, "split": "train", "timestamp": "2025-09-15T18:42:23.837380", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2233675141, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([2, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([3, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([4, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([6, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 9]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([7, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([7, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([8, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([9, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([9, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([9, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([9, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "e59de256c242487406809f7153f60e75905ac4ee5f411bc4c4a10b34d57d35fd"}
{"episode": 95, "episode_seed": 893102645, "split": "train", "timestamp": "2025-09-15T18:42:23.933441", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 893102645, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 15.0, 'agent': array([8, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": "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", "image_prev": 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"info": "{'distance': 14.0, 'agent': array([7, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([6, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([5, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([4, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 1]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 2]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 3]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([3, 4]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([3, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([2, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 6]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 7]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 8]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 9]), 'target': array([1, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "7cb5f5b08c0532ff738e6c7ba6e24c6e5c267d426fc73d333f9d7ffd4e17f18e"}
{"episode": 96, "episode_seed": 2348102761, "split": "train", "timestamp": "2025-09-15T18:42:24.047897", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2348102761, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 15.0, 'agent': array([2, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 14.0, 'agent': array([3, 1]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([3, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([3, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([3, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([8, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([9, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([9, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "c9b5a3763e6ddb25e9fc41bc3490bf5fa2d1b5468d3aa99409c7c8492a4b7589"}
{"episode": 97, "episode_seed": 2438254339, "split": "train", "timestamp": "2025-09-15T18:42:24.141004", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2438254339, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 17, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 8]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 7]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([1, 6]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([1, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([2, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([7, 4]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([7, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([8, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 3]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 2]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 1]), 'target': array([9, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "3a2b960a72f4de2cf82a05bacd6be9a4b4074a2c17833682d305d1a62049d3f6"}
{"episode": 98, "episode_seed": 793943861, "split": "train", "timestamp": "2025-09-15T18:42:24.253322", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 793943861, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 15.0, 'agent': array([1, 2]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 14.0, 'agent': array([1, 3]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([1, 4]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([1, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([2, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([3, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([3, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([4, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([5, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([5, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([6, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([7, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([8, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 3)</answer>", "action": "('move', 3)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([9, 5]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([9, 6]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([9, 7]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([9, 8]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([9, 9]), 'target': array([9, 9]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "713762f4e2db3f20bfcbaca7f9b300ca8ecdb5a0f1c2f6176e05dce8e76756a7"}
{"episode": 99, "episode_seed": 134489564, "split": "train", "timestamp": "2025-09-15T18:42:24.368383", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<RandomToyMaze2DEnv<ToyMaze2DEnv-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1534d63bebf0>", "max_steps": 100, "abs_size": 336, "master_seed": 42, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": "shortest_path", "groundtruth_kwargs": {}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 134489564, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 21, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You are navigating a 11x11 maze. The maze consists of walls (gray) and paths (white). You are represented by a blue circle, and your goal is to reach the red target square.\n\nAvailable actions:\n1. 'move': Move in one of four directions. Format: `('move', direction)` where direction is an integer:\n   - 0=right, 1=up, 2=left, 3=down\n2. 'stop': End the navigation session. Format: `('stop', 'stop')`\n\nSuccess: You succeed if you reach the red target square.\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- To move right: `('move', 0)`\n- To move up: `('move', 1)`\n- To move left: `('move', 2)`\n- To move down: `('move', 3)`\n- To stop: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"image_prev": 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"info": "{'distance': 15.0, 'agent': array([9, 8]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"image_prev": 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"image_next": 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"info": "{'distance': 14.0, 'agent': array([9, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 13.0, 'agent': array([9, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 12.0, 'agent': array([9, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 4, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 11.0, 'agent': array([8, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 5, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([7, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 6, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([6, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 7, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([5, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 8, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([5, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 9, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 0)</answer>", "action": "('move', 0)", "reward": 0.0, "image": 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"info": "{'distance': 10.0, 'agent': array([5, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 10, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 9.0, 'agent': array([4, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 11, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 8.0, 'agent': array([3, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 12, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 7.0, 'agent': array([2, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 13, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 1)</answer>", "action": "('move', 1)", "reward": 0.0, "image": 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"info": "{'distance': 6.0, 'agent': array([1, 7]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 14, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 5.0, 'agent': array([1, 6]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 15, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 4.0, 'agent': array([1, 5]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 16, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 3.0, 'agent': array([1, 4]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 17, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 2.0, 'agent': array([1, 3]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 18, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 1.0, 'agent': array([1, 2]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 19, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', 2)</answer>", "action": "('move', 2)", "reward": 0.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 20, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": "{'distance': 0.0, 'agent': array([1, 1]), 'target': array([1, 1]), 'env_feedback': 'Action executed successfully.'}", "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "4900cb19964e610057a165ae534efaeba3efee20c6a36911f17091e7bad965a1"}