{"episode": 200, "episode_seed": 3585619732, "split": "train", "timestamp": "2025-09-15T22:38:34.831531", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3585619732, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-12.514159477897353, 14.038166533167017, 24.925450367323986])</answer>", "action": "('move', [-12.514159477897353, 14.038166533167017, 24.925450367323986])", "reward": 0.0, "image": 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"info": {"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', [-12.514159477897353, 14.038166533167017, 24.925450367323986])</answer>", "action": "('move', [-12.514159477897353, 14.038166533167017, 24.925450367323986])", "reward": 0.0, "image": 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"info": {"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', [-12.514159477897353, 14.038166533167017, 24.925450367323986])</answer>", "action": "('move', [-12.514159477897353, 14.038166533167017, 24.925450367323986])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "d01349d9453342a8f95c548346eff0517bbfaaa530d0213c7d31015497797ba5"}
{"episode": 201, "episode_seed": 1857752483, "split": "train", "timestamp": "2025-09-15T22:38:35.496259", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1857752483, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-2.6369275157311876, 0.4194045374240591, 12.384950607315497])</answer>", "action": "('move', [-2.6369275157311876, 0.4194045374240591, 12.384950607315497])", "reward": 0.0, "image": 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"info": {"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', [-2.6369275157311876, 0.4194045374240591, 12.384950607315497])</answer>", "action": "('move', [-2.6369275157311876, 0.4194045374240591, 12.384950607315497])", "reward": 0.0, "image": 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"info": {"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', [-2.6369275157311876, 0.4194045374240591, 12.384950607315497])</answer>", "action": "('move', [-2.6369275157311876, 0.4194045374240591, 12.384950607315497])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "89444722d196029a8751f0aef60654a7232cbb0ea95678110bd36669a9478946"}
{"episode": 202, "episode_seed": 450095857, "split": "train", "timestamp": "2025-09-15T22:38:36.158378", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 450095857, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-2.5166063586010528, 1.630145223753048, 12.272941778142505])</answer>", "action": "('move', [-2.5166063586010528, 1.630145223753048, 12.272941778142505])", "reward": 0.0, "image": 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"info": {"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', [-2.5166063586010528, 1.630145223753048, 12.272941778142505])</answer>", "action": "('move', [-2.5166063586010528, 1.630145223753048, 12.272941778142505])", "reward": 0.0, "image": 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"info": {"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', [-2.5166063586010528, 1.630145223753048, 12.272941778142505])</answer>", "action": "('move', [-2.5166063586010528, 1.630145223753048, 12.272941778142505])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "ffac3d30e58b440417ef1e8bd9410ab84068492db3df5dbdaa551b97eb85ae73"}
{"episode": 203, "episode_seed": 136471725, "split": "train", "timestamp": "2025-09-15T22:38:36.811356", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 136471725, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-0.9115222385708027, 2.9043946607485824, -11.80643683175841])</answer>", "action": "('move', [-0.9115222385708027, 2.9043946607485824, -11.80643683175841])", "reward": 0.0, "image": 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"info": {"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', [-0.9115222385708027, 2.9043946607485824, -11.80643683175841])</answer>", "action": "('move', [-0.9115222385708027, 2.9043946607485824, -11.80643683175841])", "reward": 0.0, "image": 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"info": {"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', [-0.9115222385708027, 2.9043946607485824, -11.80643683175841])</answer>", "action": "('move', [-0.9115222385708027, 2.9043946607485824, -11.80643683175841])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "a1e116e2b8731d24ef370fc24dc68208cb9ff94c22cf2da14176b1f911071446"}
{"episode": 204, "episode_seed": 3198206523, "split": "train", "timestamp": "2025-09-15T22:38:37.898170", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3198206523, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [2.189420344190735, 0.7570612305317761, -6.462744662989745])</answer>", "action": "('move', [2.189420344190735, 0.7570612305317761, -6.462744662989745])", "reward": 0.0, "image": 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"info": {"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', [2.189420344190735, 0.7570612305317761, -6.462744662989745])</answer>", "action": "('move', [2.189420344190735, 0.7570612305317761, -6.462744662989745])", "reward": 0.0, "image": 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"info": {"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', [2.189420344190735, 0.7570612305317761, -6.462744662989745])</answer>", "action": "('move', [2.189420344190735, 0.7570612305317761, -6.462744662989745])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "22f61b7dd77a1100ee962b52d867141795bbf9c31ab55335edcf8a723a7236d4"}
{"episode": 205, "episode_seed": 2696597379, "split": "train", "timestamp": "2025-09-15T22:38:38.551645", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2696597379, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [31.976504138024737, -98.76268667528285, -34.91777752571394])</answer>", "action": "('move', [31.976504138024737, -98.76268667528285, -34.91777752571394])", "reward": 0.0, "image": 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"info": {"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', [31.976504138024737, -98.76268667528285, -34.91777752571394])</answer>", "action": "('move', [31.976504138024737, -98.76268667528285, -34.91777752571394])", "reward": 0.0, "image": 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"info": {"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', [31.976504138024737, -98.76268667528285, -34.91777752571394])</answer>", "action": "('move', [31.976504138024737, -98.76268667528285, -34.91777752571394])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "88abd1e0245922cbbf4e79e50f424efa5542afab13c0eddc0e449fdcbd95adbd"}
{"episode": 206, "episode_seed": 1548339296, "split": "train", "timestamp": "2025-09-15T22:38:39.215915", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1548339296, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-0.9690108709931451, -0.46520334866447927, -24.35169264858185])</answer>", "action": "('move', [-0.9690108709931451, -0.46520334866447927, -24.35169264858185])", "reward": 0.0, "image": 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"info": {"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', [-0.9690108709931451, -0.46520334866447927, -24.35169264858185])</answer>", "action": "('move', [-0.9690108709931451, -0.46520334866447927, -24.35169264858185])", "reward": 0.0, "image": 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"info": {"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', [-0.9690108709931451, -0.46520334866447927, -24.35169264858185])</answer>", "action": "('move', [-0.9690108709931451, -0.46520334866447927, -24.35169264858185])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "5194220fd72fdbcfe455fca6b4ea5073c9aa17587bdb282c3db58ced2caac069"}
{"episode": 207, "episode_seed": 792072276, "split": "train", "timestamp": "2025-09-15T22:38:40.305644", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 792072276, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-0.6977625331206525, -2.4606479155392873, -52.78652279144782])</answer>", "action": "('move', [-0.6977625331206525, -2.4606479155392873, -52.78652279144782])", "reward": 0.0, "image": 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"info": {"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', [-0.6977625331206525, -2.4606479155392873, -52.78652279144782])</answer>", "action": "('move', [-0.6977625331206525, -2.4606479155392873, -52.78652279144782])", "reward": 0.0, "image": 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"info": {"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', [-0.6977625331206525, -2.4606479155392873, -52.78652279144782])</answer>", "action": "('move', [-0.6977625331206525, -2.4606479155392873, -52.78652279144782])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "5fc41d3f88a320edd663241fcc65aafceb1cc162e82b22e4765022c7ae2bb2de"}
{"episode": 208, "episode_seed": 1543228291, "split": "train", "timestamp": "2025-09-15T22:38:40.957385", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1543228291, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-5.464452151624943, 0.5995618262956146, 29.650040337426958])</answer>", "action": "('move', [-5.464452151624943, 0.5995618262956146, 29.650040337426958])", "reward": 0.0, "image": 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"info": {"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', [-5.464452151624943, 0.5995618262956146, 29.650040337426958])</answer>", "action": "('move', [-5.464452151624943, 0.5995618262956146, 29.650040337426958])", "reward": 0.0, "image": 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"info": {"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', [-5.464452151624943, 0.5995618262956146, 29.650040337426958])</answer>", "action": "('move', [-5.464452151624943, 0.5995618262956146, 29.650040337426958])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "5f59adecbeeba84f1b671c47646b7e50fd6cee3f7045372f7e96fbabdc537320"}
{"episode": 209, "episode_seed": 1272924911, "split": "train", "timestamp": "2025-09-15T22:38:41.614329", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1272924911, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-16.361143488841623, 77.33424450263671, -39.2625027790142])</answer>", "action": "('move', [-16.361143488841623, 77.33424450263671, -39.2625027790142])", "reward": 0.0, "image": 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"info": {"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', [-16.361143488841623, 77.33424450263671, -39.2625027790142])</answer>", "action": "('move', [-16.361143488841623, 77.33424450263671, -39.2625027790142])", "reward": 0.0, "image": 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"info": {"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', [-16.361143488841623, 77.33424450263671, -39.2625027790142])</answer>", "action": "('move', [-16.361143488841623, 77.33424450263671, -39.2625027790142])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "c8e93ca53469c6f6d7aae5422ba2d48d0de2dea10ab335d10d5c39dc9e427889"}
{"episode": 210, "episode_seed": 2616658922, "split": "train", "timestamp": "2025-09-15T22:38:42.276251", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2616658922, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [5.2070221918417605, -9.883758272366473, 48.45237029887636])</answer>", "action": "('move', [5.2070221918417605, -9.883758272366473, 48.45237029887636])", "reward": 0.0, "image": 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"info": {"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', [5.2070221918417605, -9.883758272366473, 48.45237029887636])</answer>", "action": "('move', [5.2070221918417605, -9.883758272366473, 48.45237029887636])", "reward": 0.0, "image": 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"info": {"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', [5.2070221918417605, -9.883758272366473, 48.45237029887636])</answer>", "action": "('move', [5.2070221918417605, -9.883758272366473, 48.45237029887636])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "4fb7d1bd137eeeb1558efa6d966a14575033f984dd88f7a186e8e3aecbaac498"}
{"episode": 211, "episode_seed": 1103667320, "split": "train", "timestamp": "2025-09-15T22:38:43.358527", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1103667320, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-1.5993136648474893, -3.1260487714990006, -34.32724120101159])</answer>", "action": "('move', [-1.5993136648474893, -3.1260487714990006, -34.32724120101159])", "reward": 0.0, "image": 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"info": {"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', [-1.5993136648474893, -3.1260487714990006, -34.32724120101159])</answer>", "action": "('move', [-1.5993136648474893, -3.1260487714990006, -34.32724120101159])", "reward": 0.0, "image": 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"info": {"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', [-1.5993136648474893, -3.1260487714990006, -34.32724120101159])</answer>", "action": "('move', [-1.5993136648474893, -3.1260487714990006, -34.32724120101159])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "6359dd85cd3ce8efa8b16019828400a6c4cff892c3ab27024ba722a46e11ade9"}
{"episode": 212, "episode_seed": 1691270295, "split": "train", "timestamp": "2025-09-15T22:38:44.022718", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1691270295, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-0.3359684946766221, 2.967891422411794, -36.15028466671148])</answer>", "action": "('move', [-0.3359684946766221, 2.967891422411794, -36.15028466671148])", "reward": 0.0, "image": 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"info": {"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', [-0.3359684946766221, 2.967891422411794, -36.15028466671148])</answer>", "action": "('move', [-0.3359684946766221, 2.967891422411794, -36.15028466671148])", "reward": 0.0, "image": 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"info": {"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', [-0.3359684946766221, 2.967891422411794, -36.15028466671148])</answer>", "action": "('move', [-0.3359684946766221, 2.967891422411794, -36.15028466671148])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "bff159937ac27a191f2953a03e445352e6f6d7e40c22544867ea9647808c1c9e"}
{"episode": 213, "episode_seed": 2920255799, "split": "train", "timestamp": "2025-09-15T22:38:44.701120", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2920255799, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [2.7028001432081648, 3.8449692016152164, 27.781411699795097])</answer>", "action": "('move', [2.7028001432081648, 3.8449692016152164, 27.781411699795097])", "reward": 0.0, "image": 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"info": {"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', [2.7028001432081648, 3.8449692016152164, 27.781411699795097])</answer>", "action": "('move', [2.7028001432081648, 3.8449692016152164, 27.781411699795097])", "reward": 0.0, "image": 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"info": {"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', [2.7028001432081648, 3.8449692016152164, 27.781411699795097])</answer>", "action": "('move', [2.7028001432081648, 3.8449692016152164, 27.781411699795097])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "190c5be5811340192aebf7347ce602b33e9441889b5cc4a79b2c35d53f3adaa5"}
{"episode": 214, "episode_seed": 1756953494, "split": "train", "timestamp": "2025-09-15T22:38:45.349296", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1756953494, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [8.137515236801113, 3.641890626847699, -24.763302686607414])</answer>", "action": "('move', [8.137515236801113, 3.641890626847699, -24.763302686607414])", "reward": 0.0, "image": 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"info": {"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', [8.137515236801113, 3.641890626847699, -24.763302686607414])</answer>", "action": "('move', [8.137515236801113, 3.641890626847699, -24.763302686607414])", "reward": 0.0, "image": 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"info": {"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', [8.137515236801113, 3.641890626847699, -24.763302686607414])</answer>", "action": "('move', [8.137515236801113, 3.641890626847699, -24.763302686607414])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "f0fe5b016c2675dfe856ca809be90ff6204c940b4aebbd2d0396ad02c77884f5"}
{"episode": 215, "episode_seed": 1213586905, "split": "train", "timestamp": "2025-09-15T22:38:46.382269", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1213586905, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [2.3900261417717847, -7.847264934966912, -34.04783745977603])</answer>", "action": "('move', [2.3900261417717847, -7.847264934966912, -34.04783745977603])", "reward": 0.0, "image": 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"info": {"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', [2.3900261417717847, -7.847264934966912, -34.04783745977603])</answer>", "action": "('move', [2.3900261417717847, -7.847264934966912, -34.04783745977603])", "reward": 0.0, "image": 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"info": {"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', [2.3900261417717847, -7.847264934966912, -34.04783745977603])</answer>", "action": "('move', [2.3900261417717847, -7.847264934966912, -34.04783745977603])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "50ec9fc268b1f13b66b3ca17084155cf540f7b67a771c6a9b3daa688fea21f86"}
{"episode": 216, "episode_seed": 2190014147, "split": "train", "timestamp": "2025-09-15T22:38:47.054086", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2190014147, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [1.6328370852647929, -1.737495631551871, 20.539234780765657])</answer>", "action": "('move', [1.6328370852647929, -1.737495631551871, 20.539234780765657])", "reward": 0.0, "image": 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"info": {"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', [1.6328370852647929, -1.737495631551871, 20.539234780765657])</answer>", "action": "('move', [1.6328370852647929, -1.737495631551871, 20.539234780765657])", "reward": 0.0, "image": 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"info": {"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', [1.6328370852647929, -1.737495631551871, 20.539234780765657])</answer>", "action": "('move', [1.6328370852647929, -1.737495631551871, 20.539234780765657])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "420f79a6dcfdcd3e531fe61f615d0d5a74e55d373c37db998890b7d0f9606d44"}
{"episode": 217, "episode_seed": 3937808071, "split": "train", "timestamp": "2025-09-15T22:38:47.711369", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3937808071, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [4.305428609135602, 2.907334909294405, -17.387089230260283])</answer>", "action": "('move', [4.305428609135602, 2.907334909294405, -17.387089230260283])", "reward": 0.0, "image": 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"info": {"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', [4.305428609135602, 2.907334909294405, -17.387089230260283])</answer>", "action": "('move', [4.305428609135602, 2.907334909294405, -17.387089230260283])", "reward": 0.0, "image": 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"info": {"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', [4.305428609135602, 2.907334909294405, -17.387089230260283])</answer>", "action": "('move', [4.305428609135602, 2.907334909294405, -17.387089230260283])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "903aca65e335af3deb2a576066ecb08e5c86e2aff2206a4204dc0baf3ca1120d"}
{"episode": 218, "episode_seed": 3050062403, "split": "train", "timestamp": "2025-09-15T22:38:48.789472", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3050062403, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [2.3681310222097913, -2.7400748834637163, -40.630025181010204])</answer>", "action": "('move', [2.3681310222097913, -2.7400748834637163, -40.630025181010204])", "reward": 0.0, "image": 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"info": {"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', [2.3681310222097913, -2.7400748834637163, -40.630025181010204])</answer>", "action": "('move', [2.3681310222097913, -2.7400748834637163, -40.630025181010204])", "reward": 0.0, "image": 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"info": {"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', [2.3681310222097913, -2.7400748834637163, -40.630025181010204])</answer>", "action": "('move', [2.3681310222097913, -2.7400748834637163, -40.630025181010204])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "9d46c531dfdb54abf8265076aa4d22e73b37c000750995c4ee4a260d70508355"}
{"episode": 219, "episode_seed": 893864163, "split": "train", "timestamp": "2025-09-15T22:38:49.467277", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 893864163, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [0.9130898455898718, -1.5657272951316037, 15.387265082843802])</answer>", "action": "('move', [0.9130898455898718, -1.5657272951316037, 15.387265082843802])", "reward": 0.0, "image": 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"info": {"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', [0.9130898455898718, -1.5657272951316037, 15.387265082843802])</answer>", "action": "('move', [0.9130898455898718, -1.5657272951316037, 15.387265082843802])", "reward": 0.0, "image": 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"info": {"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', [0.9130898455898718, -1.5657272951316037, 15.387265082843802])</answer>", "action": "('move', [0.9130898455898718, -1.5657272951316037, 15.387265082843802])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "d1eba1e1e9775a595b4e0dd0df34faf12049045221578a89914b70120e8ff2c9"}
{"episode": 220, "episode_seed": 4125428710, "split": "train", "timestamp": "2025-09-15T22:38:50.120907", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4125428710, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-16.521646887271658, 11.251682127183068, 5.920912550464824])</answer>", "action": "('move', [-16.521646887271658, 11.251682127183068, 5.920912550464824])", "reward": 0.0, "image": 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"info": {"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', [-16.521646887271658, 11.251682127183068, 5.920912550464824])</answer>", "action": "('move', [-16.521646887271658, 11.251682127183068, 5.920912550464824])", "reward": 0.0, "image": 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"info": {"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', [-16.521646887271658, 11.251682127183068, 5.920912550464824])</answer>", "action": "('move', [-16.521646887271658, 11.251682127183068, 5.920912550464824])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "ef9b0cc825e484e1554ea623e31473689c84303261991576a1bb91cf996bbb3a"}
{"episode": 221, "episode_seed": 2712362093, "split": "train", "timestamp": "2025-09-15T22:38:50.778839", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2712362093, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-2.914825295986013, 5.912126159610253, 41.0720864115547])</answer>", "action": "('move', [-2.914825295986013, 5.912126159610253, 41.0720864115547])", "reward": 0.0, "image": 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"info": {"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', [-2.914825295986013, 5.912126159610253, 41.0720864115547])</answer>", "action": "('move', [-2.914825295986013, 5.912126159610253, 41.0720864115547])", "reward": 0.0, "image": 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"info": {"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', [-2.914825295986013, 5.912126159610253, 41.0720864115547])</answer>", "action": "('move', [-2.914825295986013, 5.912126159610253, 41.0720864115547])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "81a90bf90732255b6bceeddc5a8ee8cecf1e86a14dafb4854ddd6af4ca80f0ef"}
{"episode": 222, "episode_seed": 1961172725, "split": "train", "timestamp": "2025-09-15T22:38:51.845148", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1961172725, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [15.773082581772812, 14.430159139226939, 30.615616090146887])</answer>", "action": "('move', [15.773082581772812, 14.430159139226939, 30.615616090146887])", "reward": 0.0, "image": 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"info": {"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', [15.773082581772812, 14.430159139226939, 30.615616090146887])</answer>", "action": "('move', [15.773082581772812, 14.430159139226939, 30.615616090146887])", "reward": 0.0, "image": 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"info": {"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', [15.773082581772812, 14.430159139226939, 30.615616090146887])</answer>", "action": "('move', [15.773082581772812, 14.430159139226939, 30.615616090146887])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "c41938685ac7868c0ad77236e1d2f1434a342d2f3bae7546a8a2a172a7609d13"}
{"episode": 223, "episode_seed": 3430066788, "split": "train", "timestamp": "2025-09-15T22:38:52.508776", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3430066788, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [0.7559474467282256, 0.7949015407507404, -12.66942584038344])</answer>", "action": "('move', [0.7559474467282256, 0.7949015407507404, -12.66942584038344])", "reward": 0.0, "image": 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"info": {"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', [0.7559474467282256, 0.7949015407507404, -12.66942584038344])</answer>", "action": "('move', [0.7559474467282256, 0.7949015407507404, -12.66942584038344])", "reward": 0.0, "image": 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"info": {"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', [0.7559474467282256, 0.7949015407507404, -12.66942584038344])</answer>", "action": "('move', [0.7559474467282256, 0.7949015407507404, -12.66942584038344])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "20db68b4863ba0ac2144e4a6276518aaa604781578170de71036dae5e94f578d"}
{"episode": 224, "episode_seed": 1836749301, "split": "train", "timestamp": "2025-09-15T22:38:53.171467", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1836749301, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-82.01313370727829, 89.18290119379627, -39.32936595459436])</answer>", "action": "('move', [-82.01313370727829, 89.18290119379627, -39.32936595459436])", "reward": 0.0, "image": 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"info": {"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', [-82.01313370727829, 89.18290119379627, -39.32936595459436])</answer>", "action": "('move', [-82.01313370727829, 89.18290119379627, -39.32936595459436])", "reward": 0.0, "image": 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"info": {"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', [-82.01313370727829, 89.18290119379627, -39.32936595459436])</answer>", "action": "('move', [-82.01313370727829, 89.18290119379627, -39.32936595459436])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "cef9117530ad43362ddc868e70d03abe319a7bfa3d9c35f474ba700de5ab75a8"}
{"episode": 225, "episode_seed": 2287642681, "split": "train", "timestamp": "2025-09-15T22:38:54.212074", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2287642681, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [1.3890970610717084, 11.823301236613075, -35.91868290408531])</answer>", "action": "('move', [1.3890970610717084, 11.823301236613075, -35.91868290408531])", "reward": 0.0, "image": 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"info": {"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', [1.3890970610717084, 11.823301236613075, -35.91868290408531])</answer>", "action": "('move', [1.3890970610717084, 11.823301236613075, -35.91868290408531])", "reward": 0.0, "image": 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"info": {"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', [1.3890970610717084, 11.823301236613075, -35.91868290408531])</answer>", "action": "('move', [1.3890970610717084, 11.823301236613075, -35.91868290408531])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "fb9d74d656b32b7baa8ff8b4b0e4cd81462a4a7370a35244b01e299bd7713965"}
{"episode": 226, "episode_seed": 487322881, "split": "train", "timestamp": "2025-09-15T22:38:54.866209", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 487322881, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [7.486600596114872, -14.74988403304235, 21.920163611018257])</answer>", "action": "('move', [7.486600596114872, -14.74988403304235, 21.920163611018257])", "reward": 0.0, "image": 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"info": {"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', [7.486600596114872, -14.74988403304235, 21.920163611018257])</answer>", "action": "('move', [7.486600596114872, -14.74988403304235, 21.920163611018257])", "reward": 0.0, "image": 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"info": {"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', [7.486600596114872, -14.74988403304235, 21.920163611018257])</answer>", "action": "('move', [7.486600596114872, -14.74988403304235, 21.920163611018257])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "f6cb3997c4ed60a1f3536db9c53062902cb096fac540880531f245efe01e9197"}
{"episode": 227, "episode_seed": 750732302, "split": "train", "timestamp": "2025-09-15T22:38:55.514450", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 750732302, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-4.813863943760665, 7.313558740714719, -21.168736649428087])</answer>", "action": "('move', [-4.813863943760665, 7.313558740714719, -21.168736649428087])", "reward": 0.0, "image": 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"info": {"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', [-4.813863943760665, 7.313558740714719, -21.168736649428087])</answer>", "action": "('move', [-4.813863943760665, 7.313558740714719, -21.168736649428087])", "reward": 0.0, "image": 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"info": {"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', [-4.813863943760665, 7.313558740714719, -21.168736649428087])</answer>", "action": "('move', [-4.813863943760665, 7.313558740714719, -21.168736649428087])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "f61fc8b7ac0c8fa29a28c0a16568bbb3383670bb92d4fda9a1c0a31d0e3eca39"}
{"episode": 228, "episode_seed": 935868536, "split": "train", "timestamp": "2025-09-15T22:38:56.186819", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 935868536, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [83.04304007084107, -161.6134552654388, 11.383372590213506])</answer>", "action": "('move', [83.04304007084107, -161.6134552654388, 11.383372590213506])", "reward": 0.0, "image": 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"info": {"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', [83.04304007084107, -161.6134552654388, 11.383372590213506])</answer>", "action": "('move', [83.04304007084107, -161.6134552654388, 11.383372590213506])", "reward": 0.0, "image": 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"info": {"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', [83.04304007084107, -161.6134552654388, 11.383372590213506])</answer>", "action": "('move', [83.04304007084107, -161.6134552654388, 11.383372590213506])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "5ea0a26b3f6491e390983f5b71fb564fd6aceb930fadb7bce5007981551a361c"}
{"episode": 229, "episode_seed": 762181991, "split": "train", "timestamp": "2025-09-15T22:38:57.252740", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 762181991, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [1.8122085736155527, 5.966805499111124, 10.337333313400583])</answer>", "action": "('move', [1.8122085736155527, 5.966805499111124, 10.337333313400583])", "reward": 0.0, "image": 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"info": {"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', [1.8122085736155527, 5.966805499111124, 10.337333313400583])</answer>", "action": "('move', [1.8122085736155527, 5.966805499111124, 10.337333313400583])", "reward": 0.0, "image": 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"info": {"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', [1.8122085736155527, 5.966805499111124, 10.337333313400583])</answer>", "action": "('move', [1.8122085736155527, 5.966805499111124, 10.337333313400583])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "fb99be1a499ca16ee6c8ce75897e0bfc08cb748b135604bee4b2c50761a028d3"}
{"episode": 230, "episode_seed": 4112311204, "split": "train", "timestamp": "2025-09-15T22:38:57.916840", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4112311204, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-21.03255358776185, -26.34506143431908, -9.803324610461894])</answer>", "action": "('move', [-21.03255358776185, -26.34506143431908, -9.803324610461894])", "reward": 0.0, "image": 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"info": {"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', [-21.03255358776185, -26.34506143431908, -9.803324610461894])</answer>", "action": "('move', [-21.03255358776185, -26.34506143431908, -9.803324610461894])", "reward": 0.0, "image": 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"info": {"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', [-21.03255358776185, -26.34506143431908, -9.803324610461894])</answer>", "action": "('move', [-21.03255358776185, -26.34506143431908, -9.803324610461894])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "35e853547701ef1da63c65070b6b16af830b54e0b3547d4940db0f98b6be4748"}
{"episode": 231, "episode_seed": 3151453533, "split": "train", "timestamp": "2025-09-15T22:38:58.578038", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3151453533, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [3.557876327843607, -17.32829310843922, -24.34036493240284])</answer>", "action": "('move', [3.557876327843607, -17.32829310843922, -24.34036493240284])", "reward": 0.0, "image": 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"info": {"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', [3.557876327843607, -17.32829310843922, -24.34036493240284])</answer>", "action": "('move', [3.557876327843607, -17.32829310843922, -24.34036493240284])", "reward": 0.0, "image": 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"info": {"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', [3.557876327843607, -17.32829310843922, -24.34036493240284])</answer>", "action": "('move', [3.557876327843607, -17.32829310843922, -24.34036493240284])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "144d63791202a4b558eaae311185c4c1b4b89f427a35ad16218b0a4388d1fc96"}
{"episode": 232, "episode_seed": 4051660501, "split": "train", "timestamp": "2025-09-15T22:38:59.587404", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4051660501, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-0.5480152971280118, 33.50980142957848, 0.2732024625094975])</answer>", "action": "('move', [-0.5480152971280118, 33.50980142957848, 0.2732024625094975])", "reward": 0.0, "image": 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"info": {"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', [-0.5480152971280118, 33.50980142957848, 0.2732024625094975])</answer>", "action": "('move', [-0.5480152971280118, 33.50980142957848, 0.2732024625094975])", "reward": 0.0, "image": 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"info": {"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', [-0.5480152971280118, 33.50980142957848, 0.2732024625094975])</answer>", "action": "('move', [-0.5480152971280118, 33.50980142957848, 0.2732024625094975])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "1d372d6bdf2c7558fd67591bb924ed992b05594c2457300a65b8d453039ae92b"}
{"episode": 233, "episode_seed": 1717304659, "split": "train", "timestamp": "2025-09-15T22:39:00.253333", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1717304659, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [8.251667520410969, 0.23748275133600336, -29.283108756049405])</answer>", "action": "('move', [8.251667520410969, 0.23748275133600336, -29.283108756049405])", "reward": 0.0, "image": 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"info": {"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', [8.251667520410969, 0.23748275133600336, -29.283108756049405])</answer>", "action": "('move', [8.251667520410969, 0.23748275133600336, -29.283108756049405])", "reward": 0.0, "image": 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"info": {"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', [8.251667520410969, 0.23748275133600336, -29.283108756049405])</answer>", "action": "('move', [8.251667520410969, 0.23748275133600336, -29.283108756049405])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "0db7862129eb0eb702703cd6d50032bd771cf11b6c6347298ba74d12036a4d22"}
{"episode": 234, "episode_seed": 3787406432, "split": "train", "timestamp": "2025-09-15T22:39:00.912506", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3787406432, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-8.183195773630114, -0.780084238759681, 41.21322923165491])</answer>", "action": "('move', [-8.183195773630114, -0.780084238759681, 41.21322923165491])", "reward": 0.0, "image": 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"info": {"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', [-8.183195773630114, -0.780084238759681, 41.21322923165491])</answer>", "action": "('move', [-8.183195773630114, -0.780084238759681, 41.21322923165491])", "reward": 0.0, "image": 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"info": {"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', [-8.183195773630114, -0.780084238759681, 41.21322923165491])</answer>", "action": "('move', [-8.183195773630114, -0.780084238759681, 41.21322923165491])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "afaa31825b50a898821748b2ba184ee1c866250c5ec50be63cf3fa914ff548a2"}
{"episode": 235, "episode_seed": 2199273802, "split": "train", "timestamp": "2025-09-15T22:39:01.570857", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2199273802, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-9.175715198109392, 2.0952254089972464, 57.41752051501019])</answer>", "action": "('move', [-9.175715198109392, 2.0952254089972464, 57.41752051501019])", "reward": 0.0, "image": 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"info": {"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', [-9.175715198109392, 2.0952254089972464, 57.41752051501019])</answer>", "action": "('move', [-9.175715198109392, 2.0952254089972464, 57.41752051501019])", "reward": 0.0, "image": 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"info": {"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', [-9.175715198109392, 2.0952254089972464, 57.41752051501019])</answer>", "action": "('move', [-9.175715198109392, 2.0952254089972464, 57.41752051501019])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "272f28f05b23c3d1c4838e03caf44b34a98ac62beb6ca2bec8ff59bc78fcb242"}
{"episode": 236, "episode_seed": 2776312228, "split": "train", "timestamp": "2025-09-15T22:39:02.608091", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2776312228, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-0.726992478148173, 0.28548499187791926, 45.75056901815909])</answer>", "action": "('move', [-0.726992478148173, 0.28548499187791926, 45.75056901815909])", "reward": 0.0, "image": 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"info": {"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', [-0.726992478148173, 0.28548499187791926, 45.75056901815909])</answer>", "action": "('move', [-0.726992478148173, 0.28548499187791926, 45.75056901815909])", "reward": 0.0, "image": 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"info": {"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', [-0.726992478148173, 0.28548499187791926, 45.75056901815909])</answer>", "action": "('move', [-0.726992478148173, 0.28548499187791926, 45.75056901815909])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "4745b5878e7dedd75a496c0e6336c80a58db771d219f028d50df1857606e1029"}
{"episode": 237, "episode_seed": 176032233, "split": "train", "timestamp": "2025-09-15T22:39:03.268614", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 176032233, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-1.4791729880234545, -2.0861175448397797, 28.851096912775706])</answer>", "action": "('move', [-1.4791729880234545, -2.0861175448397797, 28.851096912775706])", "reward": 0.0, "image": 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"info": {"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', [-1.4791729880234545, -2.0861175448397797, 28.851096912775706])</answer>", "action": "('move', [-1.4791729880234545, -2.0861175448397797, 28.851096912775706])", "reward": 0.0, "image": 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"info": {"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', [-1.4791729880234545, -2.0861175448397797, 28.851096912775706])</answer>", "action": "('move', [-1.4791729880234545, -2.0861175448397797, 28.851096912775706])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "38235e701e3a34a52dc7c46debcac60aaf217b465f283fe3eb61c6d6c07de088"}
{"episode": 238, "episode_seed": 918370591, "split": "train", "timestamp": "2025-09-15T22:39:03.941404", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 918370591, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-6.190435654433607, 1.8731602537906913, 3.7494223913022133])</answer>", "action": "('move', [-6.190435654433607, 1.8731602537906913, 3.7494223913022133])", "reward": 0.0, "image": 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"info": {"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', [-6.190435654433607, 1.8731602537906913, 3.7494223913022133])</answer>", "action": "('move', [-6.190435654433607, 1.8731602537906913, 3.7494223913022133])", "reward": 0.0, "image": 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"info": {"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', [-6.190435654433607, 1.8731602537906913, 3.7494223913022133])</answer>", "action": "('move', [-6.190435654433607, 1.8731602537906913, 3.7494223913022133])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "0f4f6e7b4d8e3be754f070efd7398ec62a0a5a1f7728275bfba7053b738c86ab"}
{"episode": 239, "episode_seed": 2036661138, "split": "train", "timestamp": "2025-09-15T22:39:04.991351", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2036661138, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-1.4455015118124084, -2.2225576244352836, -31.652263572326245])</answer>", "action": "('move', [-1.4455015118124084, -2.2225576244352836, -31.652263572326245])", "reward": 0.0, "image": 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"info": {"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', [-1.4455015118124084, -2.2225576244352836, -31.652263572326245])</answer>", "action": "('move', [-1.4455015118124084, -2.2225576244352836, -31.652263572326245])", "reward": 0.0, "image": 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"info": {"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', [-1.4455015118124084, -2.2225576244352836, -31.652263572326245])</answer>", "action": "('move', [-1.4455015118124084, -2.2225576244352836, -31.652263572326245])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "6ce4a4fdcdec58ec7798191b345522ec804620705ef61579af8e9b82079b8d07"}
{"episode": 240, "episode_seed": 2735172680, "split": "train", "timestamp": "2025-09-15T22:39:05.650543", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2735172680, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [2.765085531740534, 0.5937986945189534, 0.39651649318645354])</answer>", "action": "('move', [2.765085531740534, 0.5937986945189534, 0.39651649318645354])", "reward": 0.0, "image": 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"info": {"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', [2.765085531740534, 0.5937986945189534, 0.39651649318645354])</answer>", "action": "('move', [2.765085531740534, 0.5937986945189534, 0.39651649318645354])", "reward": 0.0, "image": 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"info": {"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', [2.765085531740534, 0.5937986945189534, 0.39651649318645354])</answer>", "action": "('move', [2.765085531740534, 0.5937986945189534, 0.39651649318645354])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "fc4fd5071cef59a32d352c1fe4e9a764e6d4df2c0cc016d030e85203a530d32c"}
{"episode": 241, "episode_seed": 46465501, "split": "train", "timestamp": "2025-09-15T22:39:06.313913", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 46465501, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [9.296407241772348, -16.856799193042892, -11.307798242189696])</answer>", "action": "('move', [9.296407241772348, -16.856799193042892, -11.307798242189696])", "reward": 0.0, "image": 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"info": {"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', [9.296407241772348, -16.856799193042892, -11.307798242189696])</answer>", "action": "('move', [9.296407241772348, -16.856799193042892, -11.307798242189696])", "reward": 0.0, "image": 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"info": {"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', [9.296407241772348, -16.856799193042892, -11.307798242189696])</answer>", "action": "('move', [9.296407241772348, -16.856799193042892, -11.307798242189696])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "5243ba8b9290a70f418da365d82e9005319b2d4d706edb6f69329477480d86a7"}
{"episode": 242, "episode_seed": 597628605, "split": "train", "timestamp": "2025-09-15T22:39:06.964547", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 597628605, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [30.747704388880887, 135.6609826352486, -25.217380580031715])</answer>", "action": "('move', [30.747704388880887, 135.6609826352486, -25.217380580031715])", "reward": 0.0, "image": 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"info": {"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', [30.747704388880887, 135.6609826352486, -25.217380580031715])</answer>", "action": "('move', [30.747704388880887, 135.6609826352486, -25.217380580031715])", "reward": 0.0, "image": 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"info": {"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', [30.747704388880887, 135.6609826352486, -25.217380580031715])</answer>", "action": "('move', [30.747704388880887, 135.6609826352486, -25.217380580031715])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "5a73ac50457889521eba3243f362597ba4c5185e1a79e7964cac114e5764f9bd"}
{"episode": 243, "episode_seed": 3440823880, "split": "train", "timestamp": "2025-09-15T22:39:07.983000", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3440823880, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-8.163291654770532, -17.91466340792198, -16.548521708300882])</answer>", "action": "('move', [-8.163291654770532, -17.91466340792198, -16.548521708300882])", "reward": 0.0, "image": 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"info": {"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', [-8.163291654770532, -17.91466340792198, -16.548521708300882])</answer>", "action": "('move', [-8.163291654770532, -17.91466340792198, -16.548521708300882])", "reward": 0.0, "image": 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"info": {"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', [-8.163291654770532, -17.91466340792198, -16.548521708300882])</answer>", "action": "('move', [-8.163291654770532, -17.91466340792198, -16.548521708300882])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "5272e9085349765337637e997d5187e149ea9c079366623824e305679549f86c"}
{"episode": 244, "episode_seed": 1970118986, "split": "train", "timestamp": "2025-09-15T22:39:08.660587", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1970118986, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [1.2107089425258561, -3.7513984349884453, 35.47164413428944])</answer>", "action": "('move', [1.2107089425258561, -3.7513984349884453, 35.47164413428944])", "reward": 0.0, "image": 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"info": {"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', [1.2107089425258561, -3.7513984349884453, 35.47164413428944])</answer>", "action": "('move', [1.2107089425258561, -3.7513984349884453, 35.47164413428944])", "reward": 0.0, "image": 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"info": {"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', [1.2107089425258561, -3.7513984349884453, 35.47164413428944])</answer>", "action": "('move', [1.2107089425258561, -3.7513984349884453, 35.47164413428944])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "918b70fbc685395c15a61f67a410d76e191e8113957f94b320064eb855000634"}
{"episode": 245, "episode_seed": 604465238, "split": "train", "timestamp": "2025-09-15T22:39:09.309292", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 604465238, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-4.61158162030608, -4.312169626737016, 57.86122553296161])</answer>", "action": "('move', [-4.61158162030608, -4.312169626737016, 57.86122553296161])", "reward": 0.0, "image": 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"info": {"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', [-4.61158162030608, -4.312169626737016, 57.86122553296161])</answer>", "action": "('move', [-4.61158162030608, -4.312169626737016, 57.86122553296161])", "reward": 0.0, "image": 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"info": {"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', [-4.61158162030608, -4.312169626737016, 57.86122553296161])</answer>", "action": "('move', [-4.61158162030608, -4.312169626737016, 57.86122553296161])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "5bc604bea319ff0041d39d5d928595db6ab89df01954944d5455b1e76441e329"}
{"episode": 246, "episode_seed": 3753213818, "split": "train", "timestamp": "2025-09-15T22:39:10.356108", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3753213818, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [4.1583978447215815, 1.988716562688305, -28.379094568115033])</answer>", "action": "('move', [4.1583978447215815, 1.988716562688305, -28.379094568115033])", "reward": 0.0, "image": 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"info": {"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', [4.1583978447215815, 1.988716562688305, -28.379094568115033])</answer>", "action": "('move', [4.1583978447215815, 1.988716562688305, -28.379094568115033])", "reward": 0.0, "image": 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"info": {"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', [4.1583978447215815, 1.988716562688305, -28.379094568115033])</answer>", "action": "('move', [4.1583978447215815, 1.988716562688305, -28.379094568115033])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "a8edfa82a1320352a4c0b6648fe42d7d9270f8513a65a840b08e79c4f230c221"}
{"episode": 247, "episode_seed": 1264972530, "split": "train", "timestamp": "2025-09-15T22:39:11.019478", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1264972530, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [10.257017471686074, 16.753669575225423, -7.813897283578778])</answer>", "action": "('move', [10.257017471686074, 16.753669575225423, -7.813897283578778])", "reward": 0.0, "image": 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"info": {"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', [10.257017471686074, 16.753669575225423, -7.813897283578778])</answer>", "action": "('move', [10.257017471686074, 16.753669575225423, -7.813897283578778])", "reward": 0.0, "image": 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"info": {"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', [10.257017471686074, 16.753669575225423, -7.813897283578778])</answer>", "action": "('move', [10.257017471686074, 16.753669575225423, -7.813897283578778])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "7d9c4c32b077e7f64eeff040854783847104ca419a6f67243a163343c9d1dc43"}
{"episode": 248, "episode_seed": 1110035600, "split": "train", "timestamp": "2025-09-15T22:39:11.673349", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1110035600, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [3.0104121747735237, 1.3842345840495602, 11.293087261091122])</answer>", "action": "('move', [3.0104121747735237, 1.3842345840495602, 11.293087261091122])", "reward": 0.0, "image": 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"info": {"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', [3.0104121747735237, 1.3842345840495602, 11.293087261091122])</answer>", "action": "('move', [3.0104121747735237, 1.3842345840495602, 11.293087261091122])", "reward": 0.0, "image": 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"info": {"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', [3.0104121747735237, 1.3842345840495602, 11.293087261091122])</answer>", "action": "('move', [3.0104121747735237, 1.3842345840495602, 11.293087261091122])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "d29b3091861e3c2d5ecce18162808198a79645600ae96b7bbb0a5d87f6f31957"}
{"episode": 249, "episode_seed": 3669550067, "split": "train", "timestamp": "2025-09-15T22:39:12.330846", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3669550067, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-25.784183558205957, -111.35613812305934, -53.7013818518754])</answer>", "action": "('move', [-25.784183558205957, -111.35613812305934, -53.7013818518754])", "reward": 0.0, "image": 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"info": {"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', [-25.784183558205957, -111.35613812305934, -53.7013818518754])</answer>", "action": "('move', [-25.784183558205957, -111.35613812305934, -53.7013818518754])", "reward": 0.0, "image": 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"info": {"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', [-25.784183558205957, -111.35613812305934, -53.7013818518754])</answer>", "action": "('move', [-25.784183558205957, -111.35613812305934, -53.7013818518754])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "7173894c13b65873e11e16c45fe2913b5793c4ac5469f399833d41c920612df9"}
{"episode": 250, "episode_seed": 2855513716, "split": "train", "timestamp": "2025-09-15T22:39:13.369988", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2855513716, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-1.5276549282892276, 4.630381076475135, 52.345816699191026])</answer>", "action": "('move', [-1.5276549282892276, 4.630381076475135, 52.345816699191026])", "reward": 0.0, "image": 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"info": {"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', [-1.5276549282892276, 4.630381076475135, 52.345816699191026])</answer>", "action": "('move', [-1.5276549282892276, 4.630381076475135, 52.345816699191026])", "reward": 0.0, "image": 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"info": {"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', [-1.5276549282892276, 4.630381076475135, 52.345816699191026])</answer>", "action": "('move', [-1.5276549282892276, 4.630381076475135, 52.345816699191026])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "3a3e32cc2a7b036e74ce43774392f4df359c3ddddb0cc399985f790fae546db3"}
{"episode": 251, "episode_seed": 3435943240, "split": "train", "timestamp": "2025-09-15T22:39:14.028031", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3435943240, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [4.663944920854282, -3.5412281128990126, -48.23803973895408])</answer>", "action": "('move', [4.663944920854282, -3.5412281128990126, -48.23803973895408])", "reward": 0.0, "image": 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"info": {"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', [4.663944920854282, -3.5412281128990126, -48.23803973895408])</answer>", "action": "('move', [4.663944920854282, -3.5412281128990126, -48.23803973895408])", "reward": 0.0, "image": 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"info": {"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', [4.663944920854282, -3.5412281128990126, -48.23803973895408])</answer>", "action": "('move', [4.663944920854282, -3.5412281128990126, -48.23803973895408])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "03028b50deb5756007691c678bdfd0c9c6de5a7455e6237f05ae50590fecd763"}
{"episode": 252, "episode_seed": 3705158067, "split": "train", "timestamp": "2025-09-15T22:39:14.687039", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3705158067, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-2.1569927918648504, -3.569315366208721, 55.42202216399816])</answer>", "action": "('move', [-2.1569927918648504, -3.569315366208721, 55.42202216399816])", "reward": 0.0, "image": 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"info": {"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', [-2.1569927918648504, -3.569315366208721, 55.42202216399816])</answer>", "action": "('move', [-2.1569927918648504, -3.569315366208721, 55.42202216399816])", "reward": 0.0, "image": 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"info": {"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', [-2.1569927918648504, -3.569315366208721, 55.42202216399816])</answer>", "action": "('move', [-2.1569927918648504, -3.569315366208721, 55.42202216399816])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "98cc527d12e789781b4869cd808029a81aa4ccb1eed92fe0db26ac8783907a35"}
{"episode": 253, "episode_seed": 1661247026, "split": "train", "timestamp": "2025-09-15T22:39:15.752697", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1661247026, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [6.708938970239561, -7.490580076672707, 26.616135579039753])</answer>", "action": "('move', [6.708938970239561, -7.490580076672707, 26.616135579039753])", "reward": 0.0, "image": 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"info": {"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', [6.708938970239561, -7.490580076672707, 26.616135579039753])</answer>", "action": "('move', [6.708938970239561, -7.490580076672707, 26.616135579039753])", "reward": 0.0, "image": 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"info": {"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', [6.708938970239561, -7.490580076672707, 26.616135579039753])</answer>", "action": "('move', [6.708938970239561, -7.490580076672707, 26.616135579039753])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "df3988bbb0c0f26a2a5c43fd5a1655add339a109ba7951c0d1509296dac1a1db"}
{"episode": 254, "episode_seed": 639297554, "split": "train", "timestamp": "2025-09-15T22:39:16.395156", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 639297554, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [7.540951993602378, 23.499279540283414, -51.940650201175885])</answer>", "action": "('move', [7.540951993602378, 23.499279540283414, -51.940650201175885])", "reward": 0.0, "image": 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"info": {"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', [7.540951993602378, 23.499279540283414, -51.940650201175885])</answer>", "action": "('move', [7.540951993602378, 23.499279540283414, -51.940650201175885])", "reward": 0.0, "image": 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"info": {"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', [7.540951993602378, 23.499279540283414, -51.940650201175885])</answer>", "action": "('move', [7.540951993602378, 23.499279540283414, -51.940650201175885])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "44cc9bf1865db7b47fe4941d67c98e3311aa8a9502af97e8a07b9aa86b53c11d"}
{"episode": 255, "episode_seed": 3603459676, "split": "train", "timestamp": "2025-09-15T22:39:17.054607", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3603459676, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [1.027411501817627, -3.9982980399541845, 6.865290640314261])</answer>", "action": "('move', [1.027411501817627, -3.9982980399541845, 6.865290640314261])", "reward": 0.0, "image": 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"info": {"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', [1.027411501817627, -3.9982980399541845, 6.865290640314261])</answer>", "action": "('move', [1.027411501817627, -3.9982980399541845, 6.865290640314261])", "reward": 0.0, "image": 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"info": {"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', [1.027411501817627, -3.9982980399541845, 6.865290640314261])</answer>", "action": "('move', [1.027411501817627, -3.9982980399541845, 6.865290640314261])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "309f75f422f9b0be0ee06b7078de40ab9dcaec819283f45588ad6f63b311d9d0"}
{"episode": 256, "episode_seed": 2417851677, "split": "train", "timestamp": "2025-09-15T22:39:17.716554", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2417851677, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [2.518959506236708, 1.9402033468234026, 27.70276864295431])</answer>", "action": "('move', [2.518959506236708, 1.9402033468234026, 27.70276864295431])", "reward": 0.0, "image": 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"info": {"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', [2.518959506236708, 1.9402033468234026, 27.70276864295431])</answer>", "action": "('move', [2.518959506236708, 1.9402033468234026, 27.70276864295431])", "reward": 0.0, "image": 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"info": {"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', [2.518959506236708, 1.9402033468234026, 27.70276864295431])</answer>", "action": "('move', [2.518959506236708, 1.9402033468234026, 27.70276864295431])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "b7343bd567c952bcb2158836d0a1bc11021476f1dc553008a25682eb53c42cd5"}
{"episode": 257, "episode_seed": 1553886721, "split": "train", "timestamp": "2025-09-15T22:39:18.743667", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1553886721, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-44.14222464529255, 91.64806803824469, -30.212832833092857])</answer>", "action": "('move', [-44.14222464529255, 91.64806803824469, -30.212832833092857])", "reward": 0.0, "image": 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"info": {"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', [-44.14222464529255, 91.64806803824469, -30.212832833092857])</answer>", "action": "('move', [-44.14222464529255, 91.64806803824469, -30.212832833092857])", "reward": 0.0, "image": 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"info": {"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', [-44.14222464529255, 91.64806803824469, -30.212832833092857])</answer>", "action": "('move', [-44.14222464529255, 91.64806803824469, -30.212832833092857])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "d1c3bd3aca81317e0080e96a6809c03c89a08f0b78158d164fb04bec140ae92c"}
{"episode": 258, "episode_seed": 683566655, "split": "train", "timestamp": "2025-09-15T22:39:19.428465", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 683566655, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [0.8240818356357522, -0.3498839581277349, 44.84989700784197])</answer>", "action": "('move', [0.8240818356357522, -0.3498839581277349, 44.84989700784197])", "reward": 0.0, "image": 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"info": {"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', [0.8240818356357522, -0.3498839581277349, 44.84989700784197])</answer>", "action": "('move', [0.8240818356357522, -0.3498839581277349, 44.84989700784197])", "reward": 0.0, "image": 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"info": {"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', [0.8240818356357522, -0.3498839581277349, 44.84989700784197])</answer>", "action": "('move', [0.8240818356357522, -0.3498839581277349, 44.84989700784197])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "4bf5c02d0f237d6b3d81cd92bb7a8d73dc1c7e2e54a49f4da6a29ffca6be3fb2"}
{"episode": 259, "episode_seed": 3900866700, "split": "train", "timestamp": "2025-09-15T22:39:20.079598", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3900866700, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [11.946666145613364, 5.6791943749313525, 34.1436870846976])</answer>", "action": "('move', [11.946666145613364, 5.6791943749313525, 34.1436870846976])", "reward": 0.0, "image": 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"info": {"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', [11.946666145613364, 5.6791943749313525, 34.1436870846976])</answer>", "action": "('move', [11.946666145613364, 5.6791943749313525, 34.1436870846976])", "reward": 0.0, "image": 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"info": {"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', [11.946666145613364, 5.6791943749313525, 34.1436870846976])</answer>", "action": "('move', [11.946666145613364, 5.6791943749313525, 34.1436870846976])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "eefaca2b5b968ea1ecc32e318c15f40db2fc9ed8453a59929692a1c363f766a5"}
{"episode": 260, "episode_seed": 742578050, "split": "train", "timestamp": "2025-09-15T22:39:20.729494", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 742578050, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [6.762861946502219, 26.675921587233276, -40.117608586913924])</answer>", "action": "('move', [6.762861946502219, 26.675921587233276, -40.117608586913924])", "reward": 0.0, "image": 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"info": {"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', [6.762861946502219, 26.675921587233276, -40.117608586913924])</answer>", "action": "('move', [6.762861946502219, 26.675921587233276, -40.117608586913924])", "reward": 0.0, "image": 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"info": {"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', [6.762861946502219, 26.675921587233276, -40.117608586913924])</answer>", "action": "('move', [6.762861946502219, 26.675921587233276, -40.117608586913924])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "8bd7085069f66e4abac950608cc69b2f8a8546ed05993b9cf02d247bc5f9643a"}
{"episode": 261, "episode_seed": 981842379, "split": "train", "timestamp": "2025-09-15T22:39:21.750755", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 981842379, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [15.453560165279768, -22.289561501743155, 25.842733146042445])</answer>", "action": "('move', [15.453560165279768, -22.289561501743155, 25.842733146042445])", "reward": 0.0, "image": 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"info": {"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', [15.453560165279768, -22.289561501743155, 25.842733146042445])</answer>", "action": "('move', [15.453560165279768, -22.289561501743155, 25.842733146042445])", "reward": 0.0, "image": 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"info": {"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', [15.453560165279768, -22.289561501743155, 25.842733146042445])</answer>", "action": "('move', [15.453560165279768, -22.289561501743155, 25.842733146042445])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "dfe6ac32119875c346a2d5097c9df31793f32eed46a95fa956cb63266e9181a9"}
{"episode": 262, "episode_seed": 446775213, "split": "train", "timestamp": "2025-09-15T22:39:22.406881", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 446775213, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [13.886789277747399, 7.338212088133022, -30.266669874851136])</answer>", "action": "('move', [13.886789277747399, 7.338212088133022, -30.266669874851136])", "reward": 0.0, "image": 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"info": {"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', [13.886789277747399, 7.338212088133022, -30.266669874851136])</answer>", "action": "('move', [13.886789277747399, 7.338212088133022, -30.266669874851136])", "reward": 0.0, "image": 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"info": {"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', [13.886789277747399, 7.338212088133022, -30.266669874851136])</answer>", "action": "('move', [13.886789277747399, 7.338212088133022, -30.266669874851136])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "b52a457b218a09b6fb63d4a3be37cf3c535a182fe8db36af12637ba0f3647d57"}
{"episode": 263, "episode_seed": 2078512706, "split": "train", "timestamp": "2025-09-15T22:39:23.072165", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2078512706, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-4.887719620569372, 5.460233108045904, 40.75732725205506])</answer>", "action": "('move', [-4.887719620569372, 5.460233108045904, 40.75732725205506])", "reward": 0.0, "image": 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"info": {"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', [-4.887719620569372, 5.460233108045904, 40.75732725205506])</answer>", "action": "('move', [-4.887719620569372, 5.460233108045904, 40.75732725205506])", "reward": 0.0, "image": 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"info": {"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', [-4.887719620569372, 5.460233108045904, 40.75732725205506])</answer>", "action": "('move', [-4.887719620569372, 5.460233108045904, 40.75732725205506])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "9f8da9c600361b2488145ef11c5d93b3f5346d96a82627339235812f07ca735c"}
{"episode": 264, "episode_seed": 871610313, "split": "train", "timestamp": "2025-09-15T22:39:24.096160", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 871610313, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [30.019151776332496, -32.31617881116998, -37.49626224106165])</answer>", "action": "('move', [30.019151776332496, -32.31617881116998, -37.49626224106165])", "reward": 0.0, "image": 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"info": {"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', [30.019151776332496, -32.31617881116998, -37.49626224106165])</answer>", "action": "('move', [30.019151776332496, -32.31617881116998, -37.49626224106165])", "reward": 0.0, "image": 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"info": {"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', [30.019151776332496, -32.31617881116998, -37.49626224106165])</answer>", "action": "('move', [30.019151776332496, -32.31617881116998, -37.49626224106165])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "7cb19d481bb5b73c008feb30bc3becb0913cececb548cf8405bd0052afae6209"}
{"episode": 265, "episode_seed": 2827253048, "split": "train", "timestamp": "2025-09-15T22:39:24.744881", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2827253048, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-18.279343484982913, 6.662841098435365, 7.80449634340179])</answer>", "action": "('move', [-18.279343484982913, 6.662841098435365, 7.80449634340179])", "reward": 0.0, "image": 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"info": {"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', [-18.279343484982913, 6.662841098435365, 7.80449634340179])</answer>", "action": "('move', [-18.279343484982913, 6.662841098435365, 7.80449634340179])", "reward": 0.0, "image": 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"info": {"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', [-18.279343484982913, 6.662841098435365, 7.80449634340179])</answer>", "action": "('move', [-18.279343484982913, 6.662841098435365, 7.80449634340179])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "d1c66799aff73320bf2003425d2b723e6f5b28a32e61df0c3f8eab28d6f05b4a"}
{"episode": 266, "episode_seed": 1955023153, "split": "train", "timestamp": "2025-09-15T22:39:25.395932", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1955023153, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-31.690582863689105, 26.14757071346833, 38.09298987809416])</answer>", "action": "('move', [-31.690582863689105, 26.14757071346833, 38.09298987809416])", "reward": 0.0, "image": 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"info": {"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', [-31.690582863689105, 26.14757071346833, 38.09298987809416])</answer>", "action": "('move', [-31.690582863689105, 26.14757071346833, 38.09298987809416])", "reward": 0.0, "image": 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"info": {"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', [-31.690582863689105, 26.14757071346833, 38.09298987809416])</answer>", "action": "('move', [-31.690582863689105, 26.14757071346833, 38.09298987809416])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "17fd532d825e0e349d9040ba98a88270e51a2e3b98663a113b30466504ebe98a"}
{"episode": 267, "episode_seed": 927987208, "split": "train", "timestamp": "2025-09-15T22:39:26.044087", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 927987208, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [32.38616146886191, -21.833148433973815, -50.39202641731515])</answer>", "action": "('move', [32.38616146886191, -21.833148433973815, -50.39202641731515])", "reward": 0.0, "image": 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"info": {"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', [32.38616146886191, -21.833148433973815, -50.39202641731515])</answer>", "action": "('move', [32.38616146886191, -21.833148433973815, -50.39202641731515])", "reward": 0.0, "image": 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"info": {"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', [32.38616146886191, -21.833148433973815, -50.39202641731515])</answer>", "action": "('move', [32.38616146886191, -21.833148433973815, -50.39202641731515])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "9d1b14448e8ec0e51707cf32c6ac55a4de205b0065900a8c156640563bd99d7b"}
{"episode": 268, "episode_seed": 3412673257, "split": "train", "timestamp": "2025-09-15T22:39:27.095985", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3412673257, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [1.6142240041062481, 1.0402235685123717, -28.38465773624833])</answer>", "action": "('move', [1.6142240041062481, 1.0402235685123717, -28.38465773624833])", "reward": 0.0, "image": 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"info": {"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', [1.6142240041062481, 1.0402235685123717, -28.38465773624833])</answer>", "action": "('move', [1.6142240041062481, 1.0402235685123717, -28.38465773624833])", "reward": 0.0, "image": 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"info": {"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', [1.6142240041062481, 1.0402235685123717, -28.38465773624833])</answer>", "action": "('move', [1.6142240041062481, 1.0402235685123717, -28.38465773624833])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "b49260ef2b0a227fbbaa798b2a415972961334472e3879a8c25452277d36c9d8"}
{"episode": 269, "episode_seed": 4217407140, "split": "train", "timestamp": "2025-09-15T22:39:27.772799", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4217407140, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [3.551542170832382, 1.6793322865793792, -28.16713932746139])</answer>", "action": "('move', [3.551542170832382, 1.6793322865793792, -28.16713932746139])", "reward": 0.0, "image": 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"info": {"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', [3.551542170832382, 1.6793322865793792, -28.16713932746139])</answer>", "action": "('move', [3.551542170832382, 1.6793322865793792, -28.16713932746139])", "reward": 0.0, "image": 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"info": {"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', [3.551542170832382, 1.6793322865793792, -28.16713932746139])</answer>", "action": "('move', [3.551542170832382, 1.6793322865793792, -28.16713932746139])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "576a78185aef1b6b74e31671b2ee6b77eef77b5d82d5faa33cfc937b111d0459"}
{"episode": 270, "episode_seed": 4255551010, "split": "train", "timestamp": "2025-09-15T22:39:28.433357", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4255551010, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-185.4607458881911, -338.471621309495, -41.352485100755906])</answer>", "action": "('move', [-185.4607458881911, -338.471621309495, -41.352485100755906])", "reward": 0.0, "image": 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"info": {"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', [-185.4607458881911, -338.471621309495, -41.352485100755906])</answer>", "action": "('move', [-185.4607458881911, -338.471621309495, -41.352485100755906])", "reward": 0.0, "image": 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"info": {"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', [-185.4607458881911, -338.471621309495, -41.352485100755906])</answer>", "action": "('move', [-185.4607458881911, -338.471621309495, -41.352485100755906])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "86a9e2d66b4794b3aea6dde541d6acf28135e47432515e310dcb018ccd90665e"}
{"episode": 271, "episode_seed": 395606538, "split": "train", "timestamp": "2025-09-15T22:39:29.459066", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 395606538, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-5.470479548065428, -1.1374792639426896, -53.253523904820014])</answer>", "action": "('move', [-5.470479548065428, -1.1374792639426896, -53.253523904820014])", "reward": 0.0, "image": 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"info": {"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', [-5.470479548065428, -1.1374792639426896, -53.253523904820014])</answer>", "action": "('move', [-5.470479548065428, -1.1374792639426896, -53.253523904820014])", "reward": 0.0, "image": 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"info": {"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', [-5.470479548065428, -1.1374792639426896, -53.253523904820014])</answer>", "action": "('move', [-5.470479548065428, -1.1374792639426896, -53.253523904820014])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "f59a85c2cf63d9752cede0b4f934d3c1209cd9c54c59aedac06aa7412952df7c"}
{"episode": 272, "episode_seed": 3457522555, "split": "train", "timestamp": "2025-09-15T22:39:30.102280", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3457522555, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [1.417902690865744, 3.8926789658828578, -4.854054232072542])</answer>", "action": "('move', [1.417902690865744, 3.8926789658828578, -4.854054232072542])", "reward": 0.0, "image": 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"info": {"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', [1.417902690865744, 3.8926789658828578, -4.854054232072542])</answer>", "action": "('move', [1.417902690865744, 3.8926789658828578, -4.854054232072542])", "reward": 0.0, "image": 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"info": {"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', [1.417902690865744, 3.8926789658828578, -4.854054232072542])</answer>", "action": "('move', [1.417902690865744, 3.8926789658828578, -4.854054232072542])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "f11737a6b6f70accd9f4f0ad04c93b214907727a8708f51adfac00fa9f673220"}
{"episode": 273, "episode_seed": 2846827124, "split": "train", "timestamp": "2025-09-15T22:39:30.748323", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2846827124, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [2.467670113122432, 4.6818234526810825, -22.26171897143354])</answer>", "action": "('move', [2.467670113122432, 4.6818234526810825, -22.26171897143354])", "reward": 0.0, "image": 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"info": {"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', [2.467670113122432, 4.6818234526810825, -22.26171897143354])</answer>", "action": "('move', [2.467670113122432, 4.6818234526810825, -22.26171897143354])", "reward": 0.0, "image": 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"info": {"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', [2.467670113122432, 4.6818234526810825, -22.26171897143354])</answer>", "action": "('move', [2.467670113122432, 4.6818234526810825, -22.26171897143354])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "cb30f372ad39af7b31574c0e1064d443128d37677668237bb20c17610081c311"}
{"episode": 274, "episode_seed": 1620986822, "split": "train", "timestamp": "2025-09-15T22:39:31.398438", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1620986822, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [1.1494574532972226, 5.961725549758953, -41.20601373400191])</answer>", "action": "('move', [1.1494574532972226, 5.961725549758953, -41.20601373400191])", "reward": 0.0, "image": 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"info": {"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', [1.1494574532972226, 5.961725549758953, -41.20601373400191])</answer>", "action": "('move', [1.1494574532972226, 5.961725549758953, -41.20601373400191])", "reward": 0.0, "image": 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"info": {"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', [1.1494574532972226, 5.961725549758953, -41.20601373400191])</answer>", "action": "('move', [1.1494574532972226, 5.961725549758953, -41.20601373400191])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "4c0cc6b22f3982e44f03290ffe06fa68d40f58d9648718e5e9c81a58a86d45cd"}
{"episode": 275, "episode_seed": 1148716663, "split": "train", "timestamp": "2025-09-15T22:39:32.441951", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1148716663, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [8.473819042756615, 5.059052062800623, -56.02177825876159])</answer>", "action": "('move', [8.473819042756615, 5.059052062800623, -56.02177825876159])", "reward": 0.0, "image": 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"info": {"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', [8.473819042756615, 5.059052062800623, -56.02177825876159])</answer>", "action": "('move', [8.473819042756615, 5.059052062800623, -56.02177825876159])", "reward": 0.0, "image": 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"info": {"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', [8.473819042756615, 5.059052062800623, -56.02177825876159])</answer>", "action": "('move', [8.473819042756615, 5.059052062800623, -56.02177825876159])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "d8665e2372c5c92239fd9958021b232cbdcfb1e9cfd73e31d2eb11e94d8e0622"}
{"episode": 276, "episode_seed": 2215075381, "split": "train", "timestamp": "2025-09-15T22:39:33.116531", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2215075381, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-1.1033866265361536, -1.8630351947978305, 23.263177069304636])</answer>", "action": "('move', [-1.1033866265361536, -1.8630351947978305, 23.263177069304636])", "reward": 0.0, "image": 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"info": {"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', [-1.1033866265361536, -1.8630351947978305, 23.263177069304636])</answer>", "action": "('move', [-1.1033866265361536, -1.8630351947978305, 23.263177069304636])", "reward": 0.0, "image": 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"info": {"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', [-1.1033866265361536, -1.8630351947978305, 23.263177069304636])</answer>", "action": "('move', [-1.1033866265361536, -1.8630351947978305, 23.263177069304636])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "e8afc65a7e1f00c17e571db9da79f725aa36e2a2e3e4455989be72176e42931a"}
{"episode": 277, "episode_seed": 2197014444, "split": "train", "timestamp": "2025-09-15T22:39:33.764280", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2197014444, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [16.7606480084112, -6.9345403051503105, 26.886456154800744])</answer>", "action": "('move', [16.7606480084112, -6.9345403051503105, 26.886456154800744])", "reward": 0.0, "image": 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"info": {"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', [16.7606480084112, -6.9345403051503105, 26.886456154800744])</answer>", "action": "('move', [16.7606480084112, -6.9345403051503105, 26.886456154800744])", "reward": 0.0, "image": 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"info": {"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', [16.7606480084112, -6.9345403051503105, 26.886456154800744])</answer>", "action": "('move', [16.7606480084112, -6.9345403051503105, 26.886456154800744])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "4f130580a4b294262fda549b1ef3c6b58c50fe628a1b01d1e2447b06e3c84b57"}
{"episode": 278, "episode_seed": 252968941, "split": "train", "timestamp": "2025-09-15T22:39:34.779437", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 252968941, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-126.5589102168409, -77.90840603014743, -58.24835745056165])</answer>", "action": "('move', [-126.5589102168409, -77.90840603014743, -58.24835745056165])", "reward": 0.0, "image": 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"info": {"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', [-126.5589102168409, -77.90840603014743, -58.24835745056165])</answer>", "action": "('move', [-126.5589102168409, -77.90840603014743, -58.24835745056165])", "reward": 0.0, "image": 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"info": {"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', [-126.5589102168409, -77.90840603014743, -58.24835745056165])</answer>", "action": "('move', [-126.5589102168409, -77.90840603014743, -58.24835745056165])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "b65258a30e5a3c030cc5b53d481fc8b539b2a8134a212e0c8e2d94e437100547"}
{"episode": 279, "episode_seed": 1753252373, "split": "train", "timestamp": "2025-09-15T22:39:35.423231", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1753252373, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [1.4815036729541464, -15.843328756212637, -28.088998183774844])</answer>", "action": "('move', [1.4815036729541464, -15.843328756212637, -28.088998183774844])", "reward": 0.0, "image": 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"info": {"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', [1.4815036729541464, -15.843328756212637, -28.088998183774844])</answer>", "action": "('move', [1.4815036729541464, -15.843328756212637, -28.088998183774844])", "reward": 0.0, "image": 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"info": {"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', [1.4815036729541464, -15.843328756212637, -28.088998183774844])</answer>", "action": "('move', [1.4815036729541464, -15.843328756212637, -28.088998183774844])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "996299c52d02a188c60b22423a1128345aaa2d3da72b7ea5d9fd60be6a421b10"}
{"episode": 280, "episode_seed": 3054132715, "split": "train", "timestamp": "2025-09-15T22:39:36.076450", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3054132715, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [10.27642536549019, 12.47742529498296, -16.74464675919242])</answer>", "action": "('move', [10.27642536549019, 12.47742529498296, -16.74464675919242])", "reward": 0.0, "image": 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"info": {"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', [10.27642536549019, 12.47742529498296, -16.74464675919242])</answer>", "action": "('move', [10.27642536549019, 12.47742529498296, -16.74464675919242])", "reward": 0.0, "image": 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"info": {"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', [10.27642536549019, 12.47742529498296, -16.74464675919242])</answer>", "action": "('move', [10.27642536549019, 12.47742529498296, -16.74464675919242])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "5e139a56a81906a75b0cdbe86f51a3d26686fd2879c8731130b283469d7e3219"}
{"episode": 281, "episode_seed": 1686940598, "split": "train", "timestamp": "2025-09-15T22:39:36.739694", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1686940598, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [3.5228155469164992, 0.595684587684876, 35.332959687356414])</answer>", "action": "('move', [3.5228155469164992, 0.595684587684876, 35.332959687356414])", "reward": 0.0, "image": 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"info": {"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', [3.5228155469164992, 0.595684587684876, 35.332959687356414])</answer>", "action": "('move', [3.5228155469164992, 0.595684587684876, 35.332959687356414])", "reward": 0.0, "image": 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"info": {"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', [3.5228155469164992, 0.595684587684876, 35.332959687356414])</answer>", "action": "('move', [3.5228155469164992, 0.595684587684876, 35.332959687356414])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "e004c82d31b00358b02565f3864f8eda7d4f87ec54f76f7af67b0d19b2328347"}
{"episode": 282, "episode_seed": 311418674, "split": "train", "timestamp": "2025-09-15T22:39:37.770487", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 311418674, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-0.8566237245084976, 9.2497519429014, 25.68558552799671])</answer>", "action": "('move', [-0.8566237245084976, 9.2497519429014, 25.68558552799671])", "reward": 0.0, "image": 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"info": {"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', [-0.8566237245084976, 9.2497519429014, 25.68558552799671])</answer>", "action": "('move', [-0.8566237245084976, 9.2497519429014, 25.68558552799671])", "reward": 0.0, "image": 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"info": {"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', [-0.8566237245084976, 9.2497519429014, 25.68558552799671])</answer>", "action": "('move', [-0.8566237245084976, 9.2497519429014, 25.68558552799671])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "777ab55b36f83b5c1f10fcefe1cbcb73bca8e530660f7ceda8bf48a45aadeac8"}
{"episode": 283, "episode_seed": 4193944013, "split": "train", "timestamp": "2025-09-15T22:39:38.421838", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4193944013, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-4.468097575780876, -1.8703679116433156, -50.27640048685132])</answer>", "action": "('move', [-4.468097575780876, -1.8703679116433156, -50.27640048685132])", "reward": 0.0, "image": 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"info": {"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', [-4.468097575780876, -1.8703679116433156, -50.27640048685132])</answer>", "action": "('move', [-4.468097575780876, -1.8703679116433156, -50.27640048685132])", "reward": 0.0, "image": 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"info": {"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', [-4.468097575780876, -1.8703679116433156, -50.27640048685132])</answer>", "action": "('move', [-4.468097575780876, -1.8703679116433156, -50.27640048685132])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "7bea307736f9b5b21ca48b7fbd99af524c0a1daeeea14fd963b1faeeb94eae0c"}
{"episode": 284, "episode_seed": 3790787899, "split": "train", "timestamp": "2025-09-15T22:39:39.059061", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3790787899, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-15.833005822174448, -2.2543724348994547, 34.12162673745767])</answer>", "action": "('move', [-15.833005822174448, -2.2543724348994547, 34.12162673745767])", "reward": 0.0, "image": 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"info": {"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', [-15.833005822174448, -2.2543724348994547, 34.12162673745767])</answer>", "action": "('move', [-15.833005822174448, -2.2543724348994547, 34.12162673745767])", "reward": 0.0, "image": 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"info": {"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', [-15.833005822174448, -2.2543724348994547, 34.12162673745767])</answer>", "action": "('move', [-15.833005822174448, -2.2543724348994547, 34.12162673745767])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "a4707e17289c89f684aa0ea30dae85b8ecd5efa000525b061acf346b28f1312c"}
{"episode": 285, "episode_seed": 1001218421, "split": "train", "timestamp": "2025-09-15T22:39:40.072038", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1001218421, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-0.38340441738980996, -0.9274969595268372, -46.617848319217636])</answer>", "action": "('move', [-0.38340441738980996, -0.9274969595268372, -46.617848319217636])", "reward": 0.0, "image": 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"info": {"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', [-0.38340441738980996, -0.9274969595268372, -46.617848319217636])</answer>", "action": "('move', [-0.38340441738980996, -0.9274969595268372, -46.617848319217636])", "reward": 0.0, "image": 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"info": {"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', [-0.38340441738980996, -0.9274969595268372, -46.617848319217636])</answer>", "action": "('move', [-0.38340441738980996, -0.9274969595268372, -46.617848319217636])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "96e40ee20dcb402443901f2b7e1243ff4b8e030b3046afa865884b159035cecf"}
{"episode": 286, "episode_seed": 3118394577, "split": "train", "timestamp": "2025-09-15T22:39:40.729408", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3118394577, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-1.6202760411228059, 18.694331592655512, 3.465043718484774])</answer>", "action": "('move', [-1.6202760411228059, 18.694331592655512, 3.465043718484774])", "reward": 0.0, "image": 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"info": {"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', [-1.6202760411228059, 18.694331592655512, 3.465043718484774])</answer>", "action": "('move', [-1.6202760411228059, 18.694331592655512, 3.465043718484774])", "reward": 0.0, "image": 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"info": {"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', [-1.6202760411228059, 18.694331592655512, 3.465043718484774])</answer>", "action": "('move', [-1.6202760411228059, 18.694331592655512, 3.465043718484774])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "8ad7b41fb27258c544cac8ac0f6983af4ce922cdde76a60db2e5816fae5e9094"}
{"episode": 287, "episode_seed": 3455901307, "split": "train", "timestamp": "2025-09-15T22:39:41.404365", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3455901307, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [20.408590673089503, -21.37230727285478, -47.54175210435408])</answer>", "action": "('move', [20.408590673089503, -21.37230727285478, -47.54175210435408])", "reward": 0.0, "image": 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"info": {"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', [20.408590673089503, -21.37230727285478, -47.54175210435408])</answer>", "action": "('move', [20.408590673089503, -21.37230727285478, -47.54175210435408])", "reward": 0.0, "image": 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"info": {"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', [20.408590673089503, -21.37230727285478, -47.54175210435408])</answer>", "action": "('move', [20.408590673089503, -21.37230727285478, -47.54175210435408])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "d69a4641143a17d4e9fb99f9a7dd4588a804504562679c598d6b835488519d31"}
{"episode": 288, "episode_seed": 3579280619, "split": "train", "timestamp": "2025-09-15T22:39:42.081581", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3579280619, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-1.8866750244186026, 2.690501152903049, 49.30601183634153])</answer>", "action": "('move', [-1.8866750244186026, 2.690501152903049, 49.30601183634153])", "reward": 0.0, "image": 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"info": {"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', [-1.8866750244186026, 2.690501152903049, 49.30601183634153])</answer>", "action": "('move', [-1.8866750244186026, 2.690501152903049, 49.30601183634153])", "reward": 0.0, "image": 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"info": {"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', [-1.8866750244186026, 2.690501152903049, 49.30601183634153])</answer>", "action": "('move', [-1.8866750244186026, 2.690501152903049, 49.30601183634153])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "647eb7e15671bfacf1c076505455f379040268b52f76a156547b535e1eafe508"}
{"episode": 289, "episode_seed": 2688210687, "split": "train", "timestamp": "2025-09-15T22:39:43.144053", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2688210687, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [1.1886861560134538, -23.569438846872163, 33.977845237631584])</answer>", "action": "('move', [1.1886861560134538, -23.569438846872163, 33.977845237631584])", "reward": 0.0, "image": 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"info": {"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', [1.1886861560134538, -23.569438846872163, 33.977845237631584])</answer>", "action": "('move', [1.1886861560134538, -23.569438846872163, 33.977845237631584])", "reward": 0.0, "image": 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"info": {"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', [1.1886861560134538, -23.569438846872163, 33.977845237631584])</answer>", "action": "('move', [1.1886861560134538, -23.569438846872163, 33.977845237631584])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "7bf84b547b28acafc2a0faa59bb90b6a2d2c003dd3d87b84c41c31604db11ed3"}
{"episode": 290, "episode_seed": 3050417551, "split": "train", "timestamp": "2025-09-15T22:39:43.805204", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3050417551, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [2.8669939307727517, -1.8544142951358162, 2.948942767871472])</answer>", "action": "('move', [2.8669939307727517, -1.8544142951358162, 2.948942767871472])", "reward": 0.0, "image": 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"info": {"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', [2.8669939307727517, -1.8544142951358162, 2.948942767871472])</answer>", "action": "('move', [2.8669939307727517, -1.8544142951358162, 2.948942767871472])", "reward": 0.0, "image": 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"info": {"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', [2.8669939307727517, -1.8544142951358162, 2.948942767871472])</answer>", "action": "('move', [2.8669939307727517, -1.8544142951358162, 2.948942767871472])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "8fb9b54b8a46da612e09c7fe7e38b16be1142173053062c1542e186fe76c0880"}
{"episode": 291, "episode_seed": 3176117980, "split": "train", "timestamp": "2025-09-15T22:39:44.451421", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3176117980, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-3.4290566988085316, 7.9999265211953094, 35.57180194594537])</answer>", "action": "('move', [-3.4290566988085316, 7.9999265211953094, 35.57180194594537])", "reward": 0.0, "image": 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"info": {"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', [-3.4290566988085316, 7.9999265211953094, 35.57180194594537])</answer>", "action": "('move', [-3.4290566988085316, 7.9999265211953094, 35.57180194594537])", "reward": 0.0, "image": 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"info": {"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', [-3.4290566988085316, 7.9999265211953094, 35.57180194594537])</answer>", "action": "('move', [-3.4290566988085316, 7.9999265211953094, 35.57180194594537])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "7020f13a70204fd8e6f48a6085d9fb48caccd888b7c329e6012c9da2c02eb327"}
{"episode": 292, "episode_seed": 2995438696, "split": "train", "timestamp": "2025-09-15T22:39:45.459628", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2995438696, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-0.8955838217370223, -5.146796591183053, -45.74979299428572])</answer>", "action": "('move', [-0.8955838217370223, -5.146796591183053, -45.74979299428572])", "reward": 0.0, "image": 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"info": {"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', [-0.8955838217370223, -5.146796591183053, -45.74979299428572])</answer>", "action": "('move', [-0.8955838217370223, -5.146796591183053, -45.74979299428572])", "reward": 0.0, "image": 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"info": {"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', [-0.8955838217370223, -5.146796591183053, -45.74979299428572])</answer>", "action": "('move', [-0.8955838217370223, -5.146796591183053, -45.74979299428572])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "732e0fee83a103262922a25d9498fcd07240d7efcd679a01f283c6c52e3c457b"}
{"episode": 293, "episode_seed": 260242681, "split": "train", "timestamp": "2025-09-15T22:39:46.129080", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 260242681, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-23.328062876175196, 6.534972001265476, 9.153770143671258])</answer>", "action": "('move', [-23.328062876175196, 6.534972001265476, 9.153770143671258])", "reward": 0.0, "image": 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"info": {"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', [-23.328062876175196, 6.534972001265476, 9.153770143671258])</answer>", "action": "('move', [-23.328062876175196, 6.534972001265476, 9.153770143671258])", "reward": 0.0, "image": 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"info": {"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', [-23.328062876175196, 6.534972001265476, 9.153770143671258])</answer>", "action": "('move', [-23.328062876175196, 6.534972001265476, 9.153770143671258])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "eeb17792012930cf2ebb364242c14855795000cdf1b347f45bcbe08435e97541"}
{"episode": 294, "episode_seed": 3994761149, "split": "train", "timestamp": "2025-09-15T22:39:46.797506", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3994761149, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [-283.32380407051863, -30.820837645715034, -11.4231936251673])</answer>", "action": "('move', [-283.32380407051863, -30.820837645715034, -11.4231936251673])", "reward": 0.0, "image": 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"info": {"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', [-283.32380407051863, -30.820837645715034, -11.4231936251673])</answer>", "action": "('move', [-283.32380407051863, -30.820837645715034, -11.4231936251673])", "reward": 0.0, "image": 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"info": {"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', [-283.32380407051863, -30.820837645715034, -11.4231936251673])</answer>", "action": "('move', [-283.32380407051863, -30.820837645715034, -11.4231936251673])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "33d61b40a283caa1917fae254d1dafdf835c05ee6df4cc0226b5c7bd7e8ee1ee"}
{"episode": 295, "episode_seed": 343062993, "split": "train", "timestamp": "2025-09-15T22:39:47.449606", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 343062993, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [7.706123754223508, 4.284138382479025, 18.459155069221037])</answer>", "action": "('move', [7.706123754223508, 4.284138382479025, 18.459155069221037])", "reward": 0.0, "image": 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"info": {"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', [7.706123754223508, 4.284138382479025, 18.459155069221037])</answer>", "action": "('move', [7.706123754223508, 4.284138382479025, 18.459155069221037])", "reward": 0.0, "image": 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"info": {"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', [7.706123754223508, 4.284138382479025, 18.459155069221037])</answer>", "action": "('move', [7.706123754223508, 4.284138382479025, 18.459155069221037])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "eb50babcf528896ee2ed000dec60e55bded8a58e690790df38eb3835b2ac252f"}
{"episode": 296, "episode_seed": 3786755624, "split": "train", "timestamp": "2025-09-15T22:39:48.466828", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3786755624, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [3.7310264210373636, -3.2226597564097053, -17.582132900894976])</answer>", "action": "('move', [3.7310264210373636, -3.2226597564097053, -17.582132900894976])", "reward": 0.0, "image": 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"info": {"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', [3.7310264210373636, -3.2226597564097053, -17.582132900894976])</answer>", "action": "('move', [3.7310264210373636, -3.2226597564097053, -17.582132900894976])", "reward": 0.0, "image": 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"info": {"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', [3.7310264210373636, -3.2226597564097053, -17.582132900894976])</answer>", "action": "('move', [3.7310264210373636, -3.2226597564097053, -17.582132900894976])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "16703159695cfc3f7d336046e61a0d19593e7152a1220d2a9c25001a38cda0d2"}
{"episode": 297, "episode_seed": 820936266, "split": "train", "timestamp": "2025-09-15T22:39:49.114214", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 820936266, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [2.4906456944442534, 9.679399596094393, -59.88658817298142])</answer>", "action": "('move', [2.4906456944442534, 9.679399596094393, -59.88658817298142])", "reward": 0.0, "image": 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"info": {"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', [2.4906456944442534, 9.679399596094393, -59.88658817298142])</answer>", "action": "('move', [2.4906456944442534, 9.679399596094393, -59.88658817298142])", "reward": 0.0, "image": 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"info": {"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', [2.4906456944442534, 9.679399596094393, -59.88658817298142])</answer>", "action": "('move', [2.4906456944442534, 9.679399596094393, -59.88658817298142])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "57d5dcfaa2757cfa07ee04f8be8553bd1fa458c8277b347e3e9b4a1f003dc48e"}
{"episode": 298, "episode_seed": 407901536, "split": "train", "timestamp": "2025-09-15T22:39:49.768144", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 407901536, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [1.6188574053713027, -2.2992695404834236, -15.979178210948078])</answer>", "action": "('move', [1.6188574053713027, -2.2992695404834236, -15.979178210948078])", "reward": 0.0, "image": 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"info": {"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', [1.6188574053713027, -2.2992695404834236, -15.979178210948078])</answer>", "action": "('move', [1.6188574053713027, -2.2992695404834236, -15.979178210948078])", "reward": 0.0, "image": 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"info": {"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', [1.6188574053713027, -2.2992695404834236, -15.979178210948078])</answer>", "action": "('move', [1.6188574053713027, -2.2992695404834236, -15.979178210948078])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "8128adf07ccfb2181d421fcefb7f2e4edec641a4fcd145e585e6c825454af667"}
{"episode": 299, "episode_seed": 3529194233, "split": "train", "timestamp": "2025-09-15T22:39:50.810709", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1485b8be2e60>", "max_steps": 10, "abs_size": 336, "master_seed": 44, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3529194233, "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": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('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', [1.4261241983597348, -0.4265996980902558, 37.69884831653368])</answer>", "action": "('move', [1.4261241983597348, -0.4265996980902558, 37.69884831653368])", "reward": 0.0, "image": 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"info": {"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', [1.4261241983597348, -0.4265996980902558, 37.69884831653368])</answer>", "action": "('move', [1.4261241983597348, -0.4265996980902558, 37.69884831653368])", "reward": 0.0, "image": 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"info": {"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', [1.4261241983597348, -0.4265996980902558, 37.69884831653368])</answer>", "action": "('move', [1.4261241983597348, -0.4265996980902558, 37.69884831653368])", "reward": 0.0, "image": 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"info": {"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>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"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": "6b31e033a88492c3ee9f8c84c2c2c5f6e317b9e9a9838f51ef87831d4c081d46"}