{"episode": 0, "episode_seed": 1608637542, "split": "train", "timestamp": "2025-09-15T22:38:34.689308", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1608637542, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [25.046453612179505, 57.706333165016055, 50.69197045596227])</answer>", "action": "('move', [25.046453612179505, 57.706333165016055, 50.69197045596227])", "reward": 0.0, "image": 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"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', [25.046453612179505, 57.706333165016055, 50.69197045596227])</answer>", "action": "('move', [25.046453612179505, 57.706333165016055, 50.69197045596227])", "reward": 0.0, "image": 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"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', [25.046453612179505, 57.706333165016055, 50.69197045596227])</answer>", "action": "('move', [25.046453612179505, 57.706333165016055, 50.69197045596227])", "reward": 0.0, "image": 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"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": "0786451cf7a31586e78b7e34d20997e8569a7d5299f9d0295387bc888bdab20b"}
{"episode": 1, "episode_seed": 3421126067, "split": "train", "timestamp": "2025-09-15T22:38:35.311721", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3421126067, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-0.2573841311398258, -2.2091648102414556, -27.267181235990922])</answer>", "action": "('move', [-0.2573841311398258, -2.2091648102414556, -27.267181235990922])", "reward": 0.0, "image": 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"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', [-0.2573841311398258, -2.2091648102414556, -27.267181235990922])</answer>", "action": "('move', [-0.2573841311398258, -2.2091648102414556, -27.267181235990922])", "reward": 0.0, "image": 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"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', [-0.2573841311398258, -2.2091648102414556, -27.267181235990922])</answer>", "action": "('move', [-0.2573841311398258, -2.2091648102414556, -27.267181235990922])", "reward": 0.0, "image": 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"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": "8712b7a525cb662f26c98add3b415c4f83d5180e18ea573f8efff27ad160dae5"}
{"episode": 2, "episode_seed": 4083286876, "split": "train", "timestamp": "2025-09-15T22:38:35.928529", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 4083286876, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [0.264097245082815, -1.4239294459576763, -0.8486911899533519])</answer>", "action": "('move', [0.264097245082815, -1.4239294459576763, -0.8486911899533519])", "reward": 0.0, "image": 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"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', [0.264097245082815, -1.4239294459576763, -0.8486911899533519])</answer>", "action": "('move', [0.264097245082815, -1.4239294459576763, -0.8486911899533519])", "reward": 0.0, "image": 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"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', [0.264097245082815, -1.4239294459576763, -0.8486911899533519])</answer>", "action": "('move', [0.264097245082815, -1.4239294459576763, -0.8486911899533519])", "reward": 0.0, "image": 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"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": "b89288d45cfc23cbbfc11e8d3090db91109c34f6622c555454936a1b36d1fb76"}
{"episode": 3, "episode_seed": 787846414, "split": "train", "timestamp": "2025-09-15T22:38:36.547310", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 787846414, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-1.483621181689122, -1.307648459914611, 27.019141982085426])</answer>", "action": "('move', [-1.483621181689122, -1.307648459914611, 27.019141982085426])", "reward": 0.0, "image": 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"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', [-1.483621181689122, -1.307648459914611, 27.019141982085426])</answer>", "action": "('move', [-1.483621181689122, -1.307648459914611, 27.019141982085426])", "reward": 0.0, "image": 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"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', [-1.483621181689122, -1.307648459914611, 27.019141982085426])</answer>", "action": "('move', [-1.483621181689122, -1.307648459914611, 27.019141982085426])", "reward": 0.0, "image": 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"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": "b9d818d638b5a4713fe599760facf0bc152763d78de6a14a7ac14d8a5ee0ba1c"}
{"episode": 4, "episode_seed": 3143890026, "split": "train", "timestamp": "2025-09-15T22:38:37.576255", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3143890026, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-3.433748148583701, 1.9679983564836832, -44.24689002789322])</answer>", "action": "('move', [-3.433748148583701, 1.9679983564836832, -44.24689002789322])", "reward": 0.0, "image": 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"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', [-3.433748148583701, 1.9679983564836832, -44.24689002789322])</answer>", "action": "('move', [-3.433748148583701, 1.9679983564836832, -44.24689002789322])", "reward": 0.0, "image": 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"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', [-3.433748148583701, 1.9679983564836832, -44.24689002789322])</answer>", "action": "('move', [-3.433748148583701, 1.9679983564836832, -44.24689002789322])", "reward": 0.0, "image": 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"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": "c6b11cd8bac46c4e7a2df8d2362c8b16665c86f69d49552cd70ec1d52089efd9"}
{"episode": 5, "episode_seed": 3348747335, "split": "train", "timestamp": "2025-09-15T22:38:38.190207", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3348747335, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [2.39417655598046, -0.7090454251166983, -31.592142133558387])</answer>", "action": "('move', [2.39417655598046, -0.7090454251166983, -31.592142133558387])", "reward": 0.0, "image": 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"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', [2.39417655598046, -0.7090454251166983, -31.592142133558387])</answer>", "action": "('move', [2.39417655598046, -0.7090454251166983, -31.592142133558387])", "reward": 0.0, "image": 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"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', [2.39417655598046, -0.7090454251166983, -31.592142133558387])</answer>", "action": "('move', [2.39417655598046, -0.7090454251166983, -31.592142133558387])", "reward": 0.0, "image": 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"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": "4b0b116224960c878482ae0361afea2496598e613dd6333411ace841050b3e62"}
{"episode": 6, "episode_seed": 2571218620, "split": "train", "timestamp": "2025-09-15T22:38:38.798820", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2571218620, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [3.8234544233925836, 6.2825810963155995, 33.60021790406174])</answer>", "action": "('move', [3.8234544233925836, 6.2825810963155995, 33.60021790406174])", "reward": 0.0, "image": 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"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', [3.8234544233925836, 6.2825810963155995, 33.60021790406174])</answer>", "action": "('move', [3.8234544233925836, 6.2825810963155995, 33.60021790406174])", "reward": 0.0, "image": 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"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', [3.8234544233925836, 6.2825810963155995, 33.60021790406174])</answer>", "action": "('move', [3.8234544233925836, 6.2825810963155995, 33.60021790406174])", "reward": 0.0, "image": 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"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": "9af7ee9ca24246fc766b33d603683044c98ecb87817bdb097ace5a2a3baeeaac"}
{"episode": 7, "episode_seed": 2563451924, "split": "train", "timestamp": "2025-09-15T22:38:39.796488", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2563451924, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [1.7021831386255173, 0.7901618323339933, 18.278208992074315])</answer>", "action": "('move', [1.7021831386255173, 0.7901618323339933, 18.278208992074315])", "reward": 0.0, "image": 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"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', [1.7021831386255173, 0.7901618323339933, 18.278208992074315])</answer>", "action": "('move', [1.7021831386255173, 0.7901618323339933, 18.278208992074315])", "reward": 0.0, "image": 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"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', [1.7021831386255173, 0.7901618323339933, 18.278208992074315])</answer>", "action": "('move', [1.7021831386255173, 0.7901618323339933, 18.278208992074315])", "reward": 0.0, "image": 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"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": "18a6475e38d85554f84e77a77e788992e403c62b9b8dedde4a2fcb185d95e6dd"}
{"episode": 8, "episode_seed": 670094950, "split": "train", "timestamp": "2025-09-15T22:38:40.408251", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 670094950, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [25.882167970660934, 33.20540548985432, 22.440446544847873])</answer>", "action": "('move', [25.882167970660934, 33.20540548985432, 22.440446544847873])", "reward": 0.0, "image": 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"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', [25.882167970660934, 33.20540548985432, 22.440446544847873])</answer>", "action": "('move', [25.882167970660934, 33.20540548985432, 22.440446544847873])", "reward": 0.0, "image": 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"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', [25.882167970660934, 33.20540548985432, 22.440446544847873])</answer>", "action": "('move', [25.882167970660934, 33.20540548985432, 22.440446544847873])", "reward": 0.0, "image": 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"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": "b6390208bf285dd3d9a546a9d72e1c4c29e243383ce5fa782fcb219087d7de7e"}
{"episode": 9, "episode_seed": 1914837113, "split": "train", "timestamp": "2025-09-15T22:38:41.017178", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1914837113, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-5.12343666232871, -4.2230592273897285, 8.76400178819596])</answer>", "action": "('move', [-5.12343666232871, -4.2230592273897285, 8.76400178819596])", "reward": 0.0, "image": 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"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', [-5.12343666232871, -4.2230592273897285, 8.76400178819596])</answer>", "action": "('move', [-5.12343666232871, -4.2230592273897285, 8.76400178819596])", "reward": 0.0, "image": 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"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', [-5.12343666232871, -4.2230592273897285, 8.76400178819596])</answer>", "action": "('move', [-5.12343666232871, -4.2230592273897285, 8.76400178819596])", "reward": 0.0, "image": 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"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": "103a57b42897eb78987481bc71beb7836421e32619746689d7efab5a56de4516"}
{"episode": 10, "episode_seed": 669991378, "split": "train", "timestamp": "2025-09-15T22:38:41.627720", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 669991378, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [10.46775581704702, 5.542826846727899, -38.30008531081345])</answer>", "action": "('move', [10.46775581704702, 5.542826846727899, -38.30008531081345])", "reward": 0.0, "image": 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"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', [10.46775581704702, 5.542826846727899, -38.30008531081345])</answer>", "action": "('move', [10.46775581704702, 5.542826846727899, -38.30008531081345])", "reward": 0.0, "image": 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"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', [10.46775581704702, 5.542826846727899, -38.30008531081345])</answer>", "action": "('move', [10.46775581704702, 5.542826846727899, -38.30008531081345])", "reward": 0.0, "image": 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"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": "d60c37a19f1546c3f28e43a116d5f178bfb2b095029ef51b767865fd915bb345"}
{"episode": 11, "episode_seed": 429389014, "split": "train", "timestamp": "2025-09-15T22:38:42.628158", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 429389014, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [5.023173273037298, 4.725748010103166, -36.870420905056484])</answer>", "action": "('move', [5.023173273037298, 4.725748010103166, -36.870420905056484])", "reward": 0.0, "image": 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"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', [5.023173273037298, 4.725748010103166, -36.870420905056484])</answer>", "action": "('move', [5.023173273037298, 4.725748010103166, -36.870420905056484])", "reward": 0.0, "image": 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"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', [5.023173273037298, 4.725748010103166, -36.870420905056484])</answer>", "action": "('move', [5.023173273037298, 4.725748010103166, -36.870420905056484])", "reward": 0.0, "image": 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"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": "d0cd41b97e8c1ced6355a7c6c16942e77107b070cc508555be7042ae08a148f0"}
{"episode": 12, "episode_seed": 249467210, "split": "train", "timestamp": "2025-09-15T22:38:43.242039", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 249467210, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-4.122137625208477, -3.1559274569676012, -39.548264188648204])</answer>", "action": "('move', [-4.122137625208477, -3.1559274569676012, -39.548264188648204])", "reward": 0.0, "image": 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"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', [-4.122137625208477, -3.1559274569676012, -39.548264188648204])</answer>", "action": "('move', [-4.122137625208477, -3.1559274569676012, -39.548264188648204])", "reward": 0.0, "image": 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"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', [-4.122137625208477, -3.1559274569676012, -39.548264188648204])</answer>", "action": "('move', [-4.122137625208477, -3.1559274569676012, -39.548264188648204])", "reward": 0.0, "image": 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"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": "d4049ef64aef9f15c41a7c359024ecabe1e592557881babf59f294637a91f8b4"}
{"episode": 13, "episode_seed": 1972458954, "split": "train", "timestamp": "2025-09-15T22:38:43.845189", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1972458954, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [294.93962869838333, 65.42347771313295, -11.901585554054614])</answer>", "action": "('move', [294.93962869838333, 65.42347771313295, -11.901585554054614])", "reward": 0.0, "image": 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"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', [294.93962869838333, 65.42347771313295, -11.901585554054614])</answer>", "action": "('move', [294.93962869838333, 65.42347771313295, -11.901585554054614])", "reward": 0.0, "image": 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"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', [294.93962869838333, 65.42347771313295, -11.901585554054614])</answer>", "action": "('move', [294.93962869838333, 65.42347771313295, -11.901585554054614])", "reward": 0.0, "image": 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"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": "a14ab50dcb100cd4f2bc91950e017fd1b6634df63308c1c067289cd0b4a5d4bb"}
{"episode": 14, "episode_seed": 3720198231, "split": "train", "timestamp": "2025-09-15T22:38:44.449665", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3720198231, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-121.94504853985119, -162.5542372218865, -23.425440854284414])</answer>", "action": "('move', [-121.94504853985119, -162.5542372218865, -23.425440854284414])", "reward": 0.0, "image": 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"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', [-121.94504853985119, -162.5542372218865, -23.425440854284414])</answer>", "action": "('move', [-121.94504853985119, -162.5542372218865, -23.425440854284414])", "reward": 0.0, "image": 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"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', [-121.94504853985119, -162.5542372218865, -23.425440854284414])</answer>", "action": "('move', [-121.94504853985119, -162.5542372218865, -23.425440854284414])", "reward": 0.0, "image": 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"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": "888ed3e5652d6e5fa74449718157d5764f686ac1d3c8dfb3b3caea24d6a475f1"}
{"episode": 15, "episode_seed": 1433267572, "split": "train", "timestamp": "2025-09-15T22:38:45.445046", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1433267572, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-0.6865126519557269, 6.5098964253452145, 8.265365776936127])</answer>", "action": "('move', [-0.6865126519557269, 6.5098964253452145, 8.265365776936127])", "reward": 0.0, "image": 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"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', [-0.6865126519557269, 6.5098964253452145, 8.265365776936127])</answer>", "action": "('move', [-0.6865126519557269, 6.5098964253452145, 8.265365776936127])", "reward": 0.0, "image": 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"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', [-0.6865126519557269, 6.5098964253452145, 8.265365776936127])</answer>", "action": "('move', [-0.6865126519557269, 6.5098964253452145, 8.265365776936127])", "reward": 0.0, "image": 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"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": "350366a1a253174200e7e803072dee8efcfe6665280a46f6ab604443e40596ee"}
{"episode": 16, "episode_seed": 2581769315, "split": "train", "timestamp": "2025-09-15T22:38:46.054380", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2581769315, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [1.8297969109701706, -1.2375799117410586, 20.094530489243038])</answer>", "action": "('move', [1.8297969109701706, -1.2375799117410586, 20.094530489243038])", "reward": 0.0, "image": 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"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', [1.8297969109701706, -1.2375799117410586, 20.094530489243038])</answer>", "action": "('move', [1.8297969109701706, -1.2375799117410586, 20.094530489243038])", "reward": 0.0, "image": 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"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', [1.8297969109701706, -1.2375799117410586, 20.094530489243038])</answer>", "action": "('move', [1.8297969109701706, -1.2375799117410586, 20.094530489243038])", "reward": 0.0, "image": 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"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": "ae9b85d4016f1b4cfe1de187b4c7cf92a1b90abba3b4327082f8fbf573e1bf57"}
{"episode": 17, "episode_seed": 613608295, "split": "train", "timestamp": "2025-09-15T22:38:46.665904", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 613608295, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [25.108171930599823, 2.1726595853803663, 49.904245096646356])</answer>", "action": "('move', [25.108171930599823, 2.1726595853803663, 49.904245096646356])", "reward": 0.0, "image": 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"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', [25.108171930599823, 2.1726595853803663, 49.904245096646356])</answer>", "action": "('move', [25.108171930599823, 2.1726595853803663, 49.904245096646356])", "reward": 0.0, "image": 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"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', [25.108171930599823, 2.1726595853803663, 49.904245096646356])</answer>", "action": "('move', [25.108171930599823, 2.1726595853803663, 49.904245096646356])", "reward": 0.0, "image": 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"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": "12c580471c31e1d0bdd0fe2f2a3c68a2e2afd42f268957f09e338a1e146f5d1f"}
{"episode": 18, "episode_seed": 3041148567, "split": "train", "timestamp": "2025-09-15T22:38:47.659883", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3041148567, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-12.475313351225234, 3.20323492275705, -37.41770727056052])</answer>", "action": "('move', [-12.475313351225234, 3.20323492275705, -37.41770727056052])", "reward": 0.0, "image": 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"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', [-12.475313351225234, 3.20323492275705, -37.41770727056052])</answer>", "action": "('move', [-12.475313351225234, 3.20323492275705, -37.41770727056052])", "reward": 0.0, "image": 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"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', [-12.475313351225234, 3.20323492275705, -37.41770727056052])</answer>", "action": "('move', [-12.475313351225234, 3.20323492275705, -37.41770727056052])", "reward": 0.0, "image": 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"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": "9ffe734235ce50a6355e6ce5ca38c2dd13699576de478011b6a7ec210219e598"}
{"episode": 19, "episode_seed": 2795544706, "split": "train", "timestamp": "2025-09-15T22:38:48.266160", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2795544706, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-9.098188929858136, -5.378207517798935, -27.410638721248763])</answer>", "action": "('move', [-9.098188929858136, -5.378207517798935, -27.410638721248763])", "reward": 0.0, "image": 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"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', [-9.098188929858136, -5.378207517798935, -27.410638721248763])</answer>", "action": "('move', [-9.098188929858136, -5.378207517798935, -27.410638721248763])", "reward": 0.0, "image": 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"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', [-9.098188929858136, -5.378207517798935, -27.410638721248763])</answer>", "action": "('move', [-9.098188929858136, -5.378207517798935, -27.410638721248763])", "reward": 0.0, "image": 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"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": "81e7030305d92e94aab8e01388cee78c7bc2712d2920024f6ba54de982c89134"}
{"episode": 20, "episode_seed": 88409749, "split": "train", "timestamp": "2025-09-15T22:38:48.878500", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 88409749, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [69.93180073492213, -35.24982513663631, 29.00309094415839])</answer>", "action": "('move', [69.93180073492213, -35.24982513663631, 29.00309094415839])", "reward": 0.0, "image": 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"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', [69.93180073492213, -35.24982513663631, 29.00309094415839])</answer>", "action": "('move', [69.93180073492213, -35.24982513663631, 29.00309094415839])", "reward": 0.0, "image": 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"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', [69.93180073492213, -35.24982513663631, 29.00309094415839])</answer>", "action": "('move', [69.93180073492213, -35.24982513663631, 29.00309094415839])", "reward": 0.0, "image": 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"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": "ff062d8bc514bda20b26672fab473c4a7d96296386ddffc67661bc0ef69fea00"}
{"episode": 21, "episode_seed": 242285876, "split": "train", "timestamp": "2025-09-15T22:38:49.486266", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 242285876, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-6.406878110571114, 4.900076870282439, -34.58312845742643])</answer>", "action": "('move', [-6.406878110571114, 4.900076870282439, -34.58312845742643])", "reward": 0.0, "image": 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"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', [-6.406878110571114, 4.900076870282439, -34.58312845742643])</answer>", "action": "('move', [-6.406878110571114, 4.900076870282439, -34.58312845742643])", "reward": 0.0, "image": 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"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', [-6.406878110571114, 4.900076870282439, -34.58312845742643])</answer>", "action": "('move', [-6.406878110571114, 4.900076870282439, -34.58312845742643])", "reward": 0.0, "image": 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"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": "46039bce45d7e79e33dafacee12952af8cfc1e1ad50266fd15b623454e3f60d0"}
{"episode": 22, "episode_seed": 4165731073, "split": "train", "timestamp": "2025-09-15T22:38:50.456394", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 4165731073, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [12.623657695411318, -0.4534464188389569, -41.744125864144024])</answer>", "action": "('move', [12.623657695411318, -0.4534464188389569, -41.744125864144024])", "reward": 0.0, "image": 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"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', [12.623657695411318, -0.4534464188389569, -41.744125864144024])</answer>", "action": "('move', [12.623657695411318, -0.4534464188389569, -41.744125864144024])", "reward": 0.0, "image": 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"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', [12.623657695411318, -0.4534464188389569, -41.744125864144024])</answer>", "action": "('move', [12.623657695411318, -0.4534464188389569, -41.744125864144024])", "reward": 0.0, "image": 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"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": "d0e8bf3c20670bd46a30b1db4f321a9e58fac6d1e7581102f8e3db796079437e"}
{"episode": 23, "episode_seed": 3100961111, "split": "train", "timestamp": "2025-09-15T22:38:51.062897", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3100961111, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-3.3976090960811476, 3.9856552722070586, -13.416561886023809])</answer>", "action": "('move', [-3.3976090960811476, 3.9856552722070586, -13.416561886023809])", "reward": 0.0, "image": 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"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', [-3.3976090960811476, 3.9856552722070586, -13.416561886023809])</answer>", "action": "('move', [-3.3976090960811476, 3.9856552722070586, -13.416561886023809])", "reward": 0.0, "image": 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"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', [-3.3976090960811476, 3.9856552722070586, -13.416561886023809])</answer>", "action": "('move', [-3.3976090960811476, 3.9856552722070586, -13.416561886023809])", "reward": 0.0, "image": 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"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": "cc44e1b2d7defca22e5cd56dd3934866848c112e2926b2ae051092130ead4814"}
{"episode": 24, "episode_seed": 3575313899, "split": "train", "timestamp": "2025-09-15T22:38:51.668571", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3575313899, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [21.73799296304274, -63.22925439925125, 36.64324337750808])</answer>", "action": "('move', [21.73799296304274, -63.22925439925125, 36.64324337750808])", "reward": 0.0, "image": 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"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', [21.73799296304274, -63.22925439925125, 36.64324337750808])</answer>", "action": "('move', [21.73799296304274, -63.22925439925125, 36.64324337750808])", "reward": 0.0, "image": 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"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', [21.73799296304274, -63.22925439925125, 36.64324337750808])</answer>", "action": "('move', [21.73799296304274, -63.22925439925125, 36.64324337750808])", "reward": 0.0, "image": 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"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": "ac9516311f7f849d932d8f9a62d253f32ca6b0e62a468620c36f8d7ac67cbb6b"}
{"episode": 25, "episode_seed": 4031053213, "split": "train", "timestamp": "2025-09-15T22:38:52.658435", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 4031053213, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [27.12050579478769, 31.251857095033284, 11.689947264707087])</answer>", "action": "('move', [27.12050579478769, 31.251857095033284, 11.689947264707087])", "reward": 0.0, "image": 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"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', [27.12050579478769, 31.251857095033284, 11.689947264707087])</answer>", "action": "('move', [27.12050579478769, 31.251857095033284, 11.689947264707087])", "reward": 0.0, "image": 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"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', [27.12050579478769, 31.251857095033284, 11.689947264707087])</answer>", "action": "('move', [27.12050579478769, 31.251857095033284, 11.689947264707087])", "reward": 0.0, "image": 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"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": "edec3b3ded73ec9d2524eeea2b415155a2e8fdbb9f1a5288b7b22daa39445db4"}
{"episode": 26, "episode_seed": 911989541, "split": "train", "timestamp": "2025-09-15T22:38:53.264305", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 911989541, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [19.053441604147263, -14.102570781371666, 27.619390202664288])</answer>", "action": "('move', [19.053441604147263, -14.102570781371666, 27.619390202664288])", "reward": 0.0, "image": 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"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', [19.053441604147263, -14.102570781371666, 27.619390202664288])</answer>", "action": "('move', [19.053441604147263, -14.102570781371666, 27.619390202664288])", "reward": 0.0, "image": 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"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', [19.053441604147263, -14.102570781371666, 27.619390202664288])</answer>", "action": "('move', [19.053441604147263, -14.102570781371666, 27.619390202664288])", "reward": 0.0, "image": 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"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": "3cab275f00fad8d4a3822f7093d1a43b220b0b38afd3473ae3553a926e1fbf61"}
{"episode": 27, "episode_seed": 3344769, "split": "train", "timestamp": "2025-09-15T22:38:53.874214", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3344769, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-7.095282716370117, -4.11478962667838, 46.185997416483566])</answer>", "action": "('move', [-7.095282716370117, -4.11478962667838, 46.185997416483566])", "reward": 0.0, "image": 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"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', [-7.095282716370117, -4.11478962667838, 46.185997416483566])</answer>", "action": "('move', [-7.095282716370117, -4.11478962667838, 46.185997416483566])", "reward": 0.0, "image": 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"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', [-7.095282716370117, -4.11478962667838, 46.185997416483566])</answer>", "action": "('move', [-7.095282716370117, -4.11478962667838, 46.185997416483566])", "reward": 0.0, "image": 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"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": "2112a1a0070e5e3b298e5f0a8695edfcd6f319a05dcf0bb0161cab9b5db88f91"}
{"episode": 28, "episode_seed": 780932287, "split": "train", "timestamp": "2025-09-15T22:38:54.482745", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 780932287, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [1.3152569392927738, -1.7859640800127485, -39.70090498528015])</answer>", "action": "('move', [1.3152569392927738, -1.7859640800127485, -39.70090498528015])", "reward": 0.0, "image": 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"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', [1.3152569392927738, -1.7859640800127485, -39.70090498528015])</answer>", "action": "('move', [1.3152569392927738, -1.7859640800127485, -39.70090498528015])", "reward": 0.0, "image": 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"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', [1.3152569392927738, -1.7859640800127485, -39.70090498528015])</answer>", "action": "('move', [1.3152569392927738, -1.7859640800127485, -39.70090498528015])", "reward": 0.0, "image": 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"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": "48b8347344d70a1a2f25f55610ba83e7cdaab9cb35f65148ccdc881dd60fe042"}
{"episode": 29, "episode_seed": 4261516219, "split": "train", "timestamp": "2025-09-15T22:38:55.453412", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 4261516219, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [1.3393039442047117, -8.5273155206676, 27.07933460137978])</answer>", "action": "('move', [1.3393039442047117, -8.5273155206676, 27.07933460137978])", "reward": 0.0, "image": 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"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', [1.3393039442047117, -8.5273155206676, 27.07933460137978])</answer>", "action": "('move', [1.3393039442047117, -8.5273155206676, 27.07933460137978])", "reward": 0.0, "image": 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"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', [1.3393039442047117, -8.5273155206676, 27.07933460137978])</answer>", "action": "('move', [1.3393039442047117, -8.5273155206676, 27.07933460137978])", "reward": 0.0, "image": 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"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": "0213fbe365713b2e13e142c3973c02ab16448aa817822aad63e5ad20a633f10c"}
{"episode": 30, "episode_seed": 787716372, "split": "train", "timestamp": "2025-09-15T22:38:56.059533", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 787716372, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-10.122642573371635, 7.759066262692328, -35.44259203449342])</answer>", "action": "('move', [-10.122642573371635, 7.759066262692328, -35.44259203449342])", "reward": 0.0, "image": 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"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', [-10.122642573371635, 7.759066262692328, -35.44259203449342])</answer>", "action": "('move', [-10.122642573371635, 7.759066262692328, -35.44259203449342])", "reward": 0.0, "image": 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"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', [-10.122642573371635, 7.759066262692328, -35.44259203449342])</answer>", "action": "('move', [-10.122642573371635, 7.759066262692328, -35.44259203449342])", "reward": 0.0, "image": 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"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": "fd7fc7047b28266a71e6c879830f1c87735fb2348ff762526bb498adf6e9631e"}
{"episode": 31, "episode_seed": 2652062880, "split": "train", "timestamp": "2025-09-15T22:38:56.668300", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2652062880, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [4.61723195956894, -3.675385627749861, -53.63483120116032])</answer>", "action": "('move', [4.61723195956894, -3.675385627749861, -53.63483120116032])", "reward": 0.0, "image": 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"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', [4.61723195956894, -3.675385627749861, -53.63483120116032])</answer>", "action": "('move', [4.61723195956894, -3.675385627749861, -53.63483120116032])", "reward": 0.0, "image": 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"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', [4.61723195956894, -3.675385627749861, -53.63483120116032])</answer>", "action": "('move', [4.61723195956894, -3.675385627749861, -53.63483120116032])", "reward": 0.0, "image": 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"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": "30860a19f989e8296bfb3a2dfc70b642e1e1c1d0b44920a045fd9cb6872d3d24"}
{"episode": 32, "episode_seed": 1306710475, "split": "train", "timestamp": "2025-09-15T22:38:57.658726", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1306710475, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-1.6285692039573287, -2.124594860426248, 17.33773826421243])</answer>", "action": "('move', [-1.6285692039573287, -2.124594860426248, 17.33773826421243])", "reward": 0.0, "image": 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"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', [-1.6285692039573287, -2.124594860426248, 17.33773826421243])</answer>", "action": "('move', [-1.6285692039573287, -2.124594860426248, 17.33773826421243])", "reward": 0.0, "image": 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"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', [-1.6285692039573287, -2.124594860426248, 17.33773826421243])</answer>", "action": "('move', [-1.6285692039573287, -2.124594860426248, 17.33773826421243])", "reward": 0.0, "image": 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"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": "7fb5fc5ec341f9656b420b4d93369d288e7b5df4db96bccc114b657eccf3e156"}
{"episode": 33, "episode_seed": 2627030329, "split": "train", "timestamp": "2025-09-15T22:38:58.264978", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2627030329, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [1.115417606041386, 1.6682149418144812, -28.62376398353204])</answer>", "action": "('move', [1.115417606041386, 1.6682149418144812, -28.62376398353204])", "reward": 0.0, "image": 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"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', [1.115417606041386, 1.6682149418144812, -28.62376398353204])</answer>", "action": "('move', [1.115417606041386, 1.6682149418144812, -28.62376398353204])", "reward": 0.0, "image": 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"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', [1.115417606041386, 1.6682149418144812, -28.62376398353204])</answer>", "action": "('move', [1.115417606041386, 1.6682149418144812, -28.62376398353204])", "reward": 0.0, "image": 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"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": "f54f0624781a62de4624dd2bfad832fe6286640156cd634793735ebfd7fda842"}
{"episode": 34, "episode_seed": 2253811733, "split": "train", "timestamp": "2025-09-15T22:38:58.871367", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2253811733, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-0.21916932384330423, 1.777582017085006, 14.9253887681186])</answer>", "action": "('move', [-0.21916932384330423, 1.777582017085006, 14.9253887681186])", "reward": 0.0, "image": 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"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', [-0.21916932384330423, 1.777582017085006, 14.9253887681186])</answer>", "action": "('move', [-0.21916932384330423, 1.777582017085006, 14.9253887681186])", "reward": 0.0, "image": 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"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', [-0.21916932384330423, 1.777582017085006, 14.9253887681186])</answer>", "action": "('move', [-0.21916932384330423, 1.777582017085006, 14.9253887681186])", "reward": 0.0, "image": 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"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": "7ad1c849b85678cc50c6c07394951faf7e40d9635070b5868baa9a4129b10272"}
{"episode": 35, "episode_seed": 30349564, "split": "train", "timestamp": "2025-09-15T22:38:59.470609", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 30349564, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [52.960219360864734, 17.040800539839317, 0.45928119765924674])</answer>", "action": "('move', [52.960219360864734, 17.040800539839317, 0.45928119765924674])", "reward": 0.0, "image": 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"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', [52.960219360864734, 17.040800539839317, 0.45928119765924674])</answer>", "action": "('move', [52.960219360864734, 17.040800539839317, 0.45928119765924674])", "reward": 0.0, "image": 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"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', [52.960219360864734, 17.040800539839317, 0.45928119765924674])</answer>", "action": "('move', [52.960219360864734, 17.040800539839317, 0.45928119765924674])", "reward": 0.0, "image": 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"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": "2af2b9da9a53897927cd26d7cd03beb40316ead48798d766dea257d6e0ad0efd"}
{"episode": 36, "episode_seed": 1855189739, "split": "train", "timestamp": "2025-09-15T22:39:00.439086", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1855189739, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-0.9428776216880896, 3.148089714480634, 16.73770842527269])</answer>", "action": "('move', [-0.9428776216880896, 3.148089714480634, 16.73770842527269])", "reward": 0.0, "image": 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"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', [-0.9428776216880896, 3.148089714480634, 16.73770842527269])</answer>", "action": "('move', [-0.9428776216880896, 3.148089714480634, 16.73770842527269])", "reward": 0.0, "image": 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"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', [-0.9428776216880896, 3.148089714480634, 16.73770842527269])</answer>", "action": "('move', [-0.9428776216880896, 3.148089714480634, 16.73770842527269])", "reward": 0.0, "image": 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"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": "9be51c68cd7d2f3406e3f391c5cf2c1c8fe30d3fcc6c9a30d9a41bad1598c5e8"}
{"episode": 37, "episode_seed": 99052376, "split": "train", "timestamp": "2025-09-15T22:39:01.046734", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 99052376, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [16.2694796072401, -10.650639837120274, 17.641452215759994])</answer>", "action": "('move', [16.2694796072401, -10.650639837120274, 17.641452215759994])", "reward": 0.0, "image": 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"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', [16.2694796072401, -10.650639837120274, 17.641452215759994])</answer>", "action": "('move', [16.2694796072401, -10.650639837120274, 17.641452215759994])", "reward": 0.0, "image": 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"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', [16.2694796072401, -10.650639837120274, 17.641452215759994])</answer>", "action": "('move', [16.2694796072401, -10.650639837120274, 17.641452215759994])", "reward": 0.0, "image": 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"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": "dd4f2009608b4604a26df38076ec6a39691cf4f1271505ce26f858a19d6e9eda"}
{"episode": 38, "episode_seed": 1250819632, "split": "train", "timestamp": "2025-09-15T22:39:01.652680", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1250819632, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-2.678495665233557, -0.7775143063951672, 27.611201599150963])</answer>", "action": "('move', [-2.678495665233557, -0.7775143063951672, 27.611201599150963])", "reward": 0.0, "image": 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"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', [-2.678495665233557, -0.7775143063951672, 27.611201599150963])</answer>", "action": "('move', [-2.678495665233557, -0.7775143063951672, 27.611201599150963])", "reward": 0.0, "image": 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"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', [-2.678495665233557, -0.7775143063951672, 27.611201599150963])</answer>", "action": "('move', [-2.678495665233557, -0.7775143063951672, 27.611201599150963])", "reward": 0.0, "image": 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"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": "36f5a2894e516f2ec5b0bb15990e6ad3e5ff1f27a2bbfa1ce9a18436fb3c901c"}
{"episode": 39, "episode_seed": 2253890010, "split": "train", "timestamp": "2025-09-15T22:39:02.620939", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2253890010, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [17.61582094653905, -2.374576478962964, -44.54386691618981])</answer>", "action": "('move', [17.61582094653905, -2.374576478962964, -44.54386691618981])", "reward": 0.0, "image": 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"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', [17.61582094653905, -2.374576478962964, -44.54386691618981])</answer>", "action": "('move', [17.61582094653905, -2.374576478962964, -44.54386691618981])", "reward": 0.0, "image": 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"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', [17.61582094653905, -2.374576478962964, -44.54386691618981])</answer>", "action": "('move', [17.61582094653905, -2.374576478962964, -44.54386691618981])", "reward": 0.0, "image": 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"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": "b5f4eed5984948e50191a534e72a0a11015c7834b5d39862161d6b0380e9d889"}
{"episode": 40, "episode_seed": 2627888186, "split": "train", "timestamp": "2025-09-15T22:39:03.233276", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2627888186, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [1.1436524819126426, -6.457188114206917, 3.8857908645130315])</answer>", "action": "('move', [1.1436524819126426, -6.457188114206917, 3.8857908645130315])", "reward": 0.0, "image": 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"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', [1.1436524819126426, -6.457188114206917, 3.8857908645130315])</answer>", "action": "('move', [1.1436524819126426, -6.457188114206917, 3.8857908645130315])", "reward": 0.0, "image": 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"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', [1.1436524819126426, -6.457188114206917, 3.8857908645130315])</answer>", "action": "('move', [1.1436524819126426, -6.457188114206917, 3.8857908645130315])", "reward": 0.0, "image": 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"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": "f852b9cce6b73efdba7a28394b55847b16943cd94d33a975c2ce20e74c53e368"}
{"episode": 41, "episode_seed": 1717389822, "split": "train", "timestamp": "2025-09-15T22:39:03.845197", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1717389822, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [2.8784398794422006, 7.532262669852269, 36.894679643213635])</answer>", "action": "('move', [2.8784398794422006, 7.532262669852269, 36.894679643213635])", "reward": 0.0, "image": 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"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', [2.8784398794422006, 7.532262669852269, 36.894679643213635])</answer>", "action": "('move', [2.8784398794422006, 7.532262669852269, 36.894679643213635])", "reward": 0.0, "image": 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"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', [2.8784398794422006, 7.532262669852269, 36.894679643213635])</answer>", "action": "('move', [2.8784398794422006, 7.532262669852269, 36.894679643213635])", "reward": 0.0, "image": 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"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": "15f6bb13e407ee06f7cccf082d823c61d896397f3596823f7ae014045ae12c37"}
{"episode": 42, "episode_seed": 599121577, "split": "train", "timestamp": "2025-09-15T22:39:04.449820", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 599121577, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-2.660188110482116, -0.9264475583912385, -35.97612717628389])</answer>", "action": "('move', [-2.660188110482116, -0.9264475583912385, -35.97612717628389])", "reward": 0.0, "image": 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"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', [-2.660188110482116, -0.9264475583912385, -35.97612717628389])</answer>", "action": "('move', [-2.660188110482116, -0.9264475583912385, -35.97612717628389])", "reward": 0.0, "image": 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"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', [-2.660188110482116, -0.9264475583912385, -35.97612717628389])</answer>", "action": "('move', [-2.660188110482116, -0.9264475583912385, -35.97612717628389])", "reward": 0.0, "image": 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"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": "4013b1207c16288b2c947b5e0eb77ec833a0a2f36200f6d299c5c1bd38f9ce4b"}
{"episode": 43, "episode_seed": 200427519, "split": "train", "timestamp": "2025-09-15T22:39:05.442081", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 200427519, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [10.218752586340267, 3.22116396008033, 15.962427409426653])</answer>", "action": "('move', [10.218752586340267, 3.22116396008033, 15.962427409426653])", "reward": 0.0, "image": 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"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', [10.218752586340267, 3.22116396008033, 15.962427409426653])</answer>", "action": "('move', [10.218752586340267, 3.22116396008033, 15.962427409426653])", "reward": 0.0, "image": 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"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', [10.218752586340267, 3.22116396008033, 15.962427409426653])</answer>", "action": "('move', [10.218752586340267, 3.22116396008033, 15.962427409426653])", "reward": 0.0, "image": 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"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": "e1164741d05052b163950d9efc837190e592bb55bbdc3c5bbe11cbdcd0a3e969"}
{"episode": 44, "episode_seed": 1254751707, "split": "train", "timestamp": "2025-09-15T22:39:06.051444", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1254751707, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-3.41072547562478, 12.876529635108438, -3.650233910621504])</answer>", "action": "('move', [-3.41072547562478, 12.876529635108438, -3.650233910621504])", "reward": 0.0, "image": 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"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', [-3.41072547562478, 12.876529635108438, -3.650233910621504])</answer>", "action": "('move', [-3.41072547562478, 12.876529635108438, -3.650233910621504])", "reward": 0.0, "image": 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"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', [-3.41072547562478, 12.876529635108438, -3.650233910621504])</answer>", "action": "('move', [-3.41072547562478, 12.876529635108438, -3.650233910621504])", "reward": 0.0, "image": 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"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": "4c4fc75d15540153e458a998dd2e87dd35755e0cf308bd8446d260d1ffa3212e"}
{"episode": 45, "episode_seed": 4182248123, "split": "train", "timestamp": "2025-09-15T22:39:06.655902", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 4182248123, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [2.606391352575651, 0.2482452092955325, 24.283878837724444])</answer>", "action": "('move', [2.606391352575651, 0.2482452092955325, 24.283878837724444])", "reward": 0.0, "image": 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"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', [2.606391352575651, 0.2482452092955325, 24.283878837724444])</answer>", "action": "('move', [2.606391352575651, 0.2482452092955325, 24.283878837724444])", "reward": 0.0, "image": 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"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', [2.606391352575651, 0.2482452092955325, 24.283878837724444])</answer>", "action": "('move', [2.606391352575651, 0.2482452092955325, 24.283878837724444])", "reward": 0.0, "image": 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"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": "de824fafa24ce8f49a00de9fa9e256f20c7c2424dfb61b8916bee5102decc8a1"}
{"episode": 46, "episode_seed": 1573512143, "split": "train", "timestamp": "2025-09-15T22:39:07.611228", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1573512143, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [2.932955147450515, -0.19523867996830865, -5.815505795207476])</answer>", "action": "('move', [2.932955147450515, -0.19523867996830865, -5.815505795207476])", "reward": 0.0, "image": 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"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', [2.932955147450515, -0.19523867996830865, -5.815505795207476])</answer>", "action": "('move', [2.932955147450515, -0.19523867996830865, -5.815505795207476])", "reward": 0.0, "image": 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"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', [2.932955147450515, -0.19523867996830865, -5.815505795207476])</answer>", "action": "('move', [2.932955147450515, -0.19523867996830865, -5.815505795207476])", "reward": 0.0, "image": 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"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": "f231c9e4c7a70f3e0b3f918c8b99ffcbe459f7cf4f30ba7ac52cf6622f9ae746"}
{"episode": 47, "episode_seed": 999745294, "split": "train", "timestamp": "2025-09-15T22:39:08.221696", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 999745294, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [2.148081209882383, -5.946633531308829, -27.030828633441455])</answer>", "action": "('move', [2.148081209882383, -5.946633531308829, -27.030828633441455])", "reward": 0.0, "image": 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"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', [2.148081209882383, -5.946633531308829, -27.030828633441455])</answer>", "action": "('move', [2.148081209882383, -5.946633531308829, -27.030828633441455])", "reward": 0.0, "image": 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"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', [2.148081209882383, -5.946633531308829, -27.030828633441455])</answer>", "action": "('move', [2.148081209882383, -5.946633531308829, -27.030828633441455])", "reward": 0.0, "image": 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"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": "b72861cfa334039d783ebac33daee813e7677ebef3457ccd1386371f66c53c82"}
{"episode": 48, "episode_seed": 1958805693, "split": "train", "timestamp": "2025-09-15T22:39:08.837008", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1958805693, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [2.3642579904425998, 7.009160643463627, 29.217305899818143])</answer>", "action": "('move', [2.3642579904425998, 7.009160643463627, 29.217305899818143])", "reward": 0.0, "image": 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"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', [2.3642579904425998, 7.009160643463627, 29.217305899818143])</answer>", "action": "('move', [2.3642579904425998, 7.009160643463627, 29.217305899818143])", "reward": 0.0, "image": 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"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', [2.3642579904425998, 7.009160643463627, 29.217305899818143])</answer>", "action": "('move', [2.3642579904425998, 7.009160643463627, 29.217305899818143])", "reward": 0.0, "image": 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"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": "aff344b3a139aa215dcc5f29de9a17c180de1b9fdcd29c6af685c4e48f88e308"}
{"episode": 49, "episode_seed": 389151677, "split": "train", "timestamp": "2025-09-15T22:39:09.443348", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 389151677, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [1.068086861108494, -2.440769011070936, -33.68608218130132])</answer>", "action": "('move', [1.068086861108494, -2.440769011070936, -33.68608218130132])", "reward": 0.0, "image": 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"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', [1.068086861108494, -2.440769011070936, -33.68608218130132])</answer>", "action": "('move', [1.068086861108494, -2.440769011070936, -33.68608218130132])", "reward": 0.0, "image": 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"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', [1.068086861108494, -2.440769011070936, -33.68608218130132])</answer>", "action": "('move', [1.068086861108494, -2.440769011070936, -33.68608218130132])", "reward": 0.0, "image": 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"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": "71a87fee1d1610d6613f81aebad998609e43f2c3269379aa35ce98c70baf9d09"}
{"episode": 50, "episode_seed": 3372305070, "split": "train", "timestamp": "2025-09-15T22:39:10.417580", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3372305070, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-7.840639442075841, 39.17857156983331, -55.8861646128337])</answer>", "action": "('move', [-7.840639442075841, 39.17857156983331, -55.8861646128337])", "reward": 0.0, "image": 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"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', [-7.840639442075841, 39.17857156983331, -55.8861646128337])</answer>", "action": "('move', [-7.840639442075841, 39.17857156983331, -55.8861646128337])", "reward": 0.0, "image": 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"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', [-7.840639442075841, 39.17857156983331, -55.8861646128337])</answer>", "action": "('move', [-7.840639442075841, 39.17857156983331, -55.8861646128337])", "reward": 0.0, "image": 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"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": "7394f214f5d983b7465005764f064290699d45f311122dfd2ac20fd292c57c62"}
{"episode": 51, "episode_seed": 2655947709, "split": "train", "timestamp": "2025-09-15T22:39:11.023597", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2655947709, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [0.46617846103692223, -4.502258170403495, 28.33578811088711])</answer>", "action": "('move', [0.46617846103692223, -4.502258170403495, 28.33578811088711])", "reward": 0.0, "image": 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"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', [0.46617846103692223, -4.502258170403495, 28.33578811088711])</answer>", "action": "('move', [0.46617846103692223, -4.502258170403495, 28.33578811088711])", "reward": 0.0, "image": 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"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', [0.46617846103692223, -4.502258170403495, 28.33578811088711])</answer>", "action": "('move', [0.46617846103692223, -4.502258170403495, 28.33578811088711])", "reward": 0.0, "image": 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"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": "3886f4bb16f851063ac67960096d8069e3ee9ba37e77978990ce7255929497d7"}
{"episode": 52, "episode_seed": 857592370, "split": "train", "timestamp": "2025-09-15T22:39:11.629441", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 857592370, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [5.566587174137926, -6.007058583929278, -40.235680936433106])</answer>", "action": "('move', [5.566587174137926, -6.007058583929278, -40.235680936433106])", "reward": 0.0, "image": 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"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', [5.566587174137926, -6.007058583929278, -40.235680936433106])</answer>", "action": "('move', [5.566587174137926, -6.007058583929278, -40.235680936433106])", "reward": 0.0, "image": 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"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', [5.566587174137926, -6.007058583929278, -40.235680936433106])</answer>", "action": "('move', [5.566587174137926, -6.007058583929278, -40.235680936433106])", "reward": 0.0, "image": 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"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": "34233ef9d7a2229a28138c1686cbabb345b7f7a770f40962efd8ad37b4256857"}
{"episode": 53, "episode_seed": 1642661739, "split": "train", "timestamp": "2025-09-15T22:39:12.586829", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1642661739, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [2.3460866023369995, 8.51768525833169, -27.615781844838086])</answer>", "action": "('move', [2.3460866023369995, 8.51768525833169, -27.615781844838086])", "reward": 0.0, "image": 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"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', [2.3460866023369995, 8.51768525833169, -27.615781844838086])</answer>", "action": "('move', [2.3460866023369995, 8.51768525833169, -27.615781844838086])", "reward": 0.0, "image": 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"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', [2.3460866023369995, 8.51768525833169, -27.615781844838086])</answer>", "action": "('move', [2.3460866023369995, 8.51768525833169, -27.615781844838086])", "reward": 0.0, "image": 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"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": "a24377ecf4a87ded18c24ead227faf26563039141ab66d9b51a44b8b22d9cec9"}
{"episode": 54, "episode_seed": 2208620086, "split": "train", "timestamp": "2025-09-15T22:39:13.203472", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2208620086, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [0.931664712874596, -1.2247798717032927, -9.887169230524421])</answer>", "action": "('move', [0.931664712874596, -1.2247798717032927, -9.887169230524421])", "reward": 0.0, "image": 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"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', [0.931664712874596, -1.2247798717032927, -9.887169230524421])</answer>", "action": "('move', [0.931664712874596, -1.2247798717032927, -9.887169230524421])", "reward": 0.0, "image": 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"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', [0.931664712874596, -1.2247798717032927, -9.887169230524421])</answer>", "action": "('move', [0.931664712874596, -1.2247798717032927, -9.887169230524421])", "reward": 0.0, "image": 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"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": "0d75ddf599b3647c2c5cb1167724beedaab7518ce2116175e42735728b7b67e4"}
{"episode": 55, "episode_seed": 4222944499, "split": "train", "timestamp": "2025-09-15T22:39:13.810647", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 4222944499, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-7.08505243229396, 4.779331523942143, 40.43775467827345])</answer>", "action": "('move', [-7.08505243229396, 4.779331523942143, 40.43775467827345])", "reward": 0.0, "image": 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"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', [-7.08505243229396, 4.779331523942143, 40.43775467827345])</answer>", "action": "('move', [-7.08505243229396, 4.779331523942143, 40.43775467827345])", "reward": 0.0, "image": 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"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', [-7.08505243229396, 4.779331523942143, 40.43775467827345])</answer>", "action": "('move', [-7.08505243229396, 4.779331523942143, 40.43775467827345])", "reward": 0.0, "image": 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"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": "104e19755f80d540397cc5bbc3de7afe1b1ef327a1ff3c2cb2f3d825e6cbc3fd"}
{"episode": 56, "episode_seed": 2544401215, "split": "train", "timestamp": "2025-09-15T22:39:14.416535", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2544401215, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-3.9159058676769214, 2.953650696744374, 54.55696643427515])</answer>", "action": "('move', [-3.9159058676769214, 2.953650696744374, 54.55696643427515])", "reward": 0.0, "image": 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"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', [-3.9159058676769214, 2.953650696744374, 54.55696643427515])</answer>", "action": "('move', [-3.9159058676769214, 2.953650696744374, 54.55696643427515])", "reward": 0.0, "image": 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"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', [-3.9159058676769214, 2.953650696744374, 54.55696643427515])</answer>", "action": "('move', [-3.9159058676769214, 2.953650696744374, 54.55696643427515])", "reward": 0.0, "image": 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"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": "70dd504a5fe5a1985e106f9062176119d7d0269e3a1e8011dc8476b478564e13"}
{"episode": 57, "episode_seed": 2004731384, "split": "train", "timestamp": "2025-09-15T22:39:15.407272", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2004731384, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [3.2977433084018757, -1.7231684845053155, -55.26006767492269])</answer>", "action": "('move', [3.2977433084018757, -1.7231684845053155, -55.26006767492269])", "reward": 0.0, "image": 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"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', [3.2977433084018757, -1.7231684845053155, -55.26006767492269])</answer>", "action": "('move', [3.2977433084018757, -1.7231684845053155, -55.26006767492269])", "reward": 0.0, "image": 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"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', [3.2977433084018757, -1.7231684845053155, -55.26006767492269])</answer>", "action": "('move', [3.2977433084018757, -1.7231684845053155, -55.26006767492269])", "reward": 0.0, "image": 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"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": "b1cf3164d1d5e89d3341bac6ab68ac286406a51f5bea6ffb7a94ca6ca9cca4ea"}
{"episode": 58, "episode_seed": 199502978, "split": "train", "timestamp": "2025-09-15T22:39:16.011977", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 199502978, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-13.677819553666836, 0.16575143553881597, 56.60362017570403])</answer>", "action": "('move', [-13.677819553666836, 0.16575143553881597, 56.60362017570403])", "reward": 0.0, "image": 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"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', [-13.677819553666836, 0.16575143553881597, 56.60362017570403])</answer>", "action": "('move', [-13.677819553666836, 0.16575143553881597, 56.60362017570403])", "reward": 0.0, "image": 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"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', [-13.677819553666836, 0.16575143553881597, 56.60362017570403])</answer>", "action": "('move', [-13.677819553666836, 0.16575143553881597, 56.60362017570403])", "reward": 0.0, "image": 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"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": "c4f64e3d46c460ee4580d7cfcae5e6c706a615361768359cd02307b23f18db98"}
{"episode": 59, "episode_seed": 3693415908, "split": "train", "timestamp": "2025-09-15T22:39:16.618735", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3693415908, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [162.27456933426546, 144.38775240548037, 11.187547392080129])</answer>", "action": "('move', [162.27456933426546, 144.38775240548037, 11.187547392080129])", "reward": 0.0, "image": 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"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', [162.27456933426546, 144.38775240548037, 11.187547392080129])</answer>", "action": "('move', [162.27456933426546, 144.38775240548037, 11.187547392080129])", "reward": 0.0, "image": 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"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', [162.27456933426546, 144.38775240548037, 11.187547392080129])</answer>", "action": "('move', [162.27456933426546, 144.38775240548037, 11.187547392080129])", "reward": 0.0, "image": 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"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": "10305f8f883af5371b59f1693c7b0b23f689c242bfdae36194a44b954e4d012c"}
{"episode": 60, "episode_seed": 2609385266, "split": "train", "timestamp": "2025-09-15T22:39:17.226489", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2609385266, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [5.067108396849283, 0.00012340228710104012, 2.954220024068301])</answer>", "action": "('move', [5.067108396849283, 0.00012340228710104012, 2.954220024068301])", "reward": 0.0, "image": 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"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', [5.067108396849283, 0.00012340228710104012, 2.954220024068301])</answer>", "action": "('move', [5.067108396849283, 0.00012340228710104012, 2.954220024068301])", "reward": 0.0, "image": 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"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', [5.067108396849283, 0.00012340228710104012, 2.954220024068301])</answer>", "action": "('move', [5.067108396849283, 0.00012340228710104012, 2.954220024068301])", "reward": 0.0, "image": 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"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": "44d92d87ad6f331fe02476ac04206168414618b28799c5b110d5bdf831b912d2"}
{"episode": 61, "episode_seed": 2921898630, "split": "train", "timestamp": "2025-09-15T22:39:18.180052", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2921898630, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-10.196300159130962, 153.65476764433694, -39.0296451614793])</answer>", "action": "('move', [-10.196300159130962, 153.65476764433694, -39.0296451614793])", "reward": 0.0, "image": 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"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', [-10.196300159130962, 153.65476764433694, -39.0296451614793])</answer>", "action": "('move', [-10.196300159130962, 153.65476764433694, -39.0296451614793])", "reward": 0.0, "image": 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"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', [-10.196300159130962, 153.65476764433694, -39.0296451614793])</answer>", "action": "('move', [-10.196300159130962, 153.65476764433694, -39.0296451614793])", "reward": 0.0, "image": 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"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": "85f3fb439e69bf1c6aed8e2db24c27e4cf4413a9fdbfba783c71acfffdc92137"}
{"episode": 62, "episode_seed": 732395540, "split": "train", "timestamp": "2025-09-15T22:39:18.786160", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 732395540, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [1.4757320814969999, 1.0019775506480462, -21.244431125497584])</answer>", "action": "('move', [1.4757320814969999, 1.0019775506480462, -21.244431125497584])", "reward": 0.0, "image": 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"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', [1.4757320814969999, 1.0019775506480462, -21.244431125497584])</answer>", "action": "('move', [1.4757320814969999, 1.0019775506480462, -21.244431125497584])", "reward": 0.0, "image": 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"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', [1.4757320814969999, 1.0019775506480462, -21.244431125497584])</answer>", "action": "('move', [1.4757320814969999, 1.0019775506480462, -21.244431125497584])", "reward": 0.0, "image": 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"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": "20cea643abbd85b9d4ae9446dc8c3738de3572629ffce5929260791dc0fc5a9c"}
{"episode": 63, "episode_seed": 1934879560, "split": "train", "timestamp": "2025-09-15T22:39:19.396037", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1934879560, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-1.1703734071679501, 3.84244107630283, 39.95888246358155])</answer>", "action": "('move', [-1.1703734071679501, 3.84244107630283, 39.95888246358155])", "reward": 0.0, "image": 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"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', [-1.1703734071679501, 3.84244107630283, 39.95888246358155])</answer>", "action": "('move', [-1.1703734071679501, 3.84244107630283, 39.95888246358155])", "reward": 0.0, "image": 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"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', [-1.1703734071679501, 3.84244107630283, 39.95888246358155])</answer>", "action": "('move', [-1.1703734071679501, 3.84244107630283, 39.95888246358155])", "reward": 0.0, "image": 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"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": "08cddc09592796075f30e7d5e903c03c37a9913c0284535ed26fc3d95d69eb4e"}
{"episode": 64, "episode_seed": 279394470, "split": "train", "timestamp": "2025-09-15T22:39:20.367759", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 279394470, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-1.2367470913497625, 7.259503613353993, -41.97460381785882])</answer>", "action": "('move', [-1.2367470913497625, 7.259503613353993, -41.97460381785882])", "reward": 0.0, "image": 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"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', [-1.2367470913497625, 7.259503613353993, -41.97460381785882])</answer>", "action": "('move', [-1.2367470913497625, 7.259503613353993, -41.97460381785882])", "reward": 0.0, "image": 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"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', [-1.2367470913497625, 7.259503613353993, -41.97460381785882])</answer>", "action": "('move', [-1.2367470913497625, 7.259503613353993, -41.97460381785882])", "reward": 0.0, "image": 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"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": "1753037a2fc4180aaf9c1fe209b422cc7120e15b603d083ee085ad6393f4b261"}
{"episode": 65, "episode_seed": 56972561, "split": "train", "timestamp": "2025-09-15T22:39:20.973616", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 56972561, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-5.716680563837577, -1.678544539956951, 6.410043739135659])</answer>", "action": "('move', [-5.716680563837577, -1.678544539956951, 6.410043739135659])", "reward": 0.0, "image": 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"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', [-5.716680563837577, -1.678544539956951, 6.410043739135659])</answer>", "action": "('move', [-5.716680563837577, -1.678544539956951, 6.410043739135659])", "reward": 0.0, "image": 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"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', [-5.716680563837577, -1.678544539956951, 6.410043739135659])</answer>", "action": "('move', [-5.716680563837577, -1.678544539956951, 6.410043739135659])", "reward": 0.0, "image": 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"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": "42e5cb06d8891e4496304dbe7a8628fb11333f816258362d940bbd87c331bdf9"}
{"episode": 66, "episode_seed": 4075432323, "split": "train", "timestamp": "2025-09-15T22:39:21.582053", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 4075432323, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-4.937035720032303, 5.607579528911592, 17.7443034988702])</answer>", "action": "('move', [-4.937035720032303, 5.607579528911592, 17.7443034988702])", "reward": 0.0, "image": 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"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', [-4.937035720032303, 5.607579528911592, 17.7443034988702])</answer>", "action": "('move', [-4.937035720032303, 5.607579528911592, 17.7443034988702])", "reward": 0.0, "image": 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"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', [-4.937035720032303, 5.607579528911592, 17.7443034988702])</answer>", "action": "('move', [-4.937035720032303, 5.607579528911592, 17.7443034988702])", "reward": 0.0, "image": 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"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": "c73f4563da9ef721f2a787d1b643ee1c46b29ccfa3a064f36c41a6329f32e4bc"}
{"episode": 67, "episode_seed": 4046725720, "split": "train", "timestamp": "2025-09-15T22:39:22.188437", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 4046725720, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-0.7828385989286826, -0.5339354915283164, 46.444331605075924])</answer>", "action": "('move', [-0.7828385989286826, -0.5339354915283164, 46.444331605075924])", "reward": 0.0, "image": 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"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', [-0.7828385989286826, -0.5339354915283164, 46.444331605075924])</answer>", "action": "('move', [-0.7828385989286826, -0.5339354915283164, 46.444331605075924])", "reward": 0.0, "image": 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"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', [-0.7828385989286826, -0.5339354915283164, 46.444331605075924])</answer>", "action": "('move', [-0.7828385989286826, -0.5339354915283164, 46.444331605075924])", "reward": 0.0, "image": 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"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": "7c6422fcf06909c36cbf368662486e3f42c6df4c9165f17046ea8e895b22b201"}
{"episode": 68, "episode_seed": 4147358011, "split": "train", "timestamp": "2025-09-15T22:39:23.146679", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 4147358011, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-54.865465844201815, 48.80392952243054, 50.65594852280799])</answer>", "action": "('move', [-54.865465844201815, 48.80392952243054, 50.65594852280799])", "reward": 0.0, "image": 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"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', [-54.865465844201815, 48.80392952243054, 50.65594852280799])</answer>", "action": "('move', [-54.865465844201815, 48.80392952243054, 50.65594852280799])", "reward": 0.0, "image": 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"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', [-54.865465844201815, 48.80392952243054, 50.65594852280799])</answer>", "action": "('move', [-54.865465844201815, 48.80392952243054, 50.65594852280799])", "reward": 0.0, "image": 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"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": "c642d860f56425c9b7513ec69bb0ab6d006d6a8d00a6fbc2fb1441998fe88ebc"}
{"episode": 69, "episode_seed": 2419304461, "split": "train", "timestamp": "2025-09-15T22:39:23.750246", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2419304461, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-5.334778729161819, -5.959020182483214, 15.72567345318268])</answer>", "action": "('move', [-5.334778729161819, -5.959020182483214, 15.72567345318268])", "reward": 0.0, "image": 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"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', [-5.334778729161819, -5.959020182483214, 15.72567345318268])</answer>", "action": "('move', [-5.334778729161819, -5.959020182483214, 15.72567345318268])", "reward": 0.0, "image": 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"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', [-5.334778729161819, -5.959020182483214, 15.72567345318268])</answer>", "action": "('move', [-5.334778729161819, -5.959020182483214, 15.72567345318268])", "reward": 0.0, "image": 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"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": "6c3a366559ec43f57912d6ca0a88293ccd66d19dc975a2f5de850e2c8e1088c7"}
{"episode": 70, "episode_seed": 3472040177, "split": "train", "timestamp": "2025-09-15T22:39:24.353101", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3472040177, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [0.7997608485544895, 1.7119426505828939, 46.008218653581594])</answer>", "action": "('move', [0.7997608485544895, 1.7119426505828939, 46.008218653581594])", "reward": 0.0, "image": 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"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', [0.7997608485544895, 1.7119426505828939, 46.008218653581594])</answer>", "action": "('move', [0.7997608485544895, 1.7119426505828939, 46.008218653581594])", "reward": 0.0, "image": 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"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', [0.7997608485544895, 1.7119426505828939, 46.008218653581594])</answer>", "action": "('move', [0.7997608485544895, 1.7119426505828939, 46.008218653581594])", "reward": 0.0, "image": 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"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": "30df1e2baed5234ba4f899e2b882ce82863b186c4dc67e9341718fe91f1a52cb"}
{"episode": 71, "episode_seed": 1655351289, "split": "train", "timestamp": "2025-09-15T22:39:25.309327", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1655351289, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [1.6563108069234875, 2.680080325388852, -15.815975469298508])</answer>", "action": "('move', [1.6563108069234875, 2.680080325388852, -15.815975469298508])", "reward": 0.0, "image": 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"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', [1.6563108069234875, 2.680080325388852, -15.815975469298508])</answer>", "action": "('move', [1.6563108069234875, 2.680080325388852, -15.815975469298508])", "reward": 0.0, "image": 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"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', [1.6563108069234875, 2.680080325388852, -15.815975469298508])</answer>", "action": "('move', [1.6563108069234875, 2.680080325388852, -15.815975469298508])", "reward": 0.0, "image": 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"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": "1085cf1c19d4151ceec8c13bbd170fa5d0ded6600ee81e2a629902047ace0c4c"}
{"episode": 72, "episode_seed": 1308306184, "split": "train", "timestamp": "2025-09-15T22:39:25.921424", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1308306184, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-9.0585655935095, 2.71718170746983, 50.437617696014])</answer>", "action": "('move', [-9.0585655935095, 2.71718170746983, 50.437617696014])", "reward": 0.0, "image": 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"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', [-9.0585655935095, 2.71718170746983, 50.437617696014])</answer>", "action": "('move', [-9.0585655935095, 2.71718170746983, 50.437617696014])", "reward": 0.0, "image": 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"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', [-9.0585655935095, 2.71718170746983, 50.437617696014])</answer>", "action": "('move', [-9.0585655935095, 2.71718170746983, 50.437617696014])", "reward": 0.0, "image": 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"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": "653895ee575856a71e65fb76a09d06c72ee104c0617bd22e6a186af7ce94e783"}
{"episode": 73, "episode_seed": 68574553, "split": "train", "timestamp": "2025-09-15T22:39:26.528716", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 68574553, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [5.005897671034899, -5.579955075853557, 29.30666910045134])</answer>", "action": "('move', [5.005897671034899, -5.579955075853557, 29.30666910045134])", "reward": 0.0, "image": 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"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', [5.005897671034899, -5.579955075853557, 29.30666910045134])</answer>", "action": "('move', [5.005897671034899, -5.579955075853557, 29.30666910045134])", "reward": 0.0, "image": 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"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', [5.005897671034899, -5.579955075853557, 29.30666910045134])</answer>", "action": "('move', [5.005897671034899, -5.579955075853557, 29.30666910045134])", "reward": 0.0, "image": 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"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": "930bfae1582735155d0bb33e02428e2b08c530f137d87affe8e605d52523c3fa"}
{"episode": 74, "episode_seed": 419498548, "split": "train", "timestamp": "2025-09-15T22:39:27.136100", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 419498548, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-1.895251802252644, 7.529287727701154, 38.4308940447155])</answer>", "action": "('move', [-1.895251802252644, 7.529287727701154, 38.4308940447155])", "reward": 0.0, "image": 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"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', [-1.895251802252644, 7.529287727701154, 38.4308940447155])</answer>", "action": "('move', [-1.895251802252644, 7.529287727701154, 38.4308940447155])", "reward": 0.0, "image": 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"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', [-1.895251802252644, 7.529287727701154, 38.4308940447155])</answer>", "action": "('move', [-1.895251802252644, 7.529287727701154, 38.4308940447155])", "reward": 0.0, "image": 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"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": "b4e2e1a5202f2b2df2bfb30bfa7b59c4847c22f19c3be450042a1c722d2a2e59"}
{"episode": 75, "episode_seed": 991681409, "split": "train", "timestamp": "2025-09-15T22:39:28.126349", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 991681409, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-7.288229714277974, 1.9097669344637873, 16.83201013033433])</answer>", "action": "('move', [-7.288229714277974, 1.9097669344637873, 16.83201013033433])", "reward": 0.0, "image": 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"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', [-7.288229714277974, 1.9097669344637873, 16.83201013033433])</answer>", "action": "('move', [-7.288229714277974, 1.9097669344637873, 16.83201013033433])", "reward": 0.0, "image": 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"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', [-7.288229714277974, 1.9097669344637873, 16.83201013033433])</answer>", "action": "('move', [-7.288229714277974, 1.9097669344637873, 16.83201013033433])", "reward": 0.0, "image": 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"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": "5678964acf69bfa9dd587836928eb72ab3166647957ae467d69e3b094359fd59"}
{"episode": 76, "episode_seed": 2938758483, "split": "train", "timestamp": "2025-09-15T22:39:28.732714", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2938758483, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [4.276768565714105, -6.844001999907902, -50.807879820278515])</answer>", "action": "('move', [4.276768565714105, -6.844001999907902, -50.807879820278515])", "reward": 0.0, "image": 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"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', [4.276768565714105, -6.844001999907902, -50.807879820278515])</answer>", "action": "('move', [4.276768565714105, -6.844001999907902, -50.807879820278515])", "reward": 0.0, "image": 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"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', [4.276768565714105, -6.844001999907902, -50.807879820278515])</answer>", "action": "('move', [4.276768565714105, -6.844001999907902, -50.807879820278515])", "reward": 0.0, "image": 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"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": "b0d69ed6d542b8730d4bab9db6cfeb58d8cd5b3b4bcbdfbc344d3d6f419aa77b"}
{"episode": 77, "episode_seed": 1035196507, "split": "train", "timestamp": "2025-09-15T22:39:29.336251", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1035196507, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [3.0366421788607476, 14.682244860637702, -35.262745316604764])</answer>", "action": "('move', [3.0366421788607476, 14.682244860637702, -35.262745316604764])", "reward": 0.0, "image": 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"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', [3.0366421788607476, 14.682244860637702, -35.262745316604764])</answer>", "action": "('move', [3.0366421788607476, 14.682244860637702, -35.262745316604764])", "reward": 0.0, "image": 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"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', [3.0366421788607476, 14.682244860637702, -35.262745316604764])</answer>", "action": "('move', [3.0366421788607476, 14.682244860637702, -35.262745316604764])", "reward": 0.0, "image": 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"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": "9d8097933bed7bbd0898f0d29d9f10ad672061ddd769cd6f3f47a4bbfb2f7495"}
{"episode": 78, "episode_seed": 1890440558, "split": "train", "timestamp": "2025-09-15T22:39:30.289287", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1890440558, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-19.592532264594404, 1.562274574170359, 13.359774277845295])</answer>", "action": "('move', [-19.592532264594404, 1.562274574170359, 13.359774277845295])", "reward": 0.0, "image": 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"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', [-19.592532264594404, 1.562274574170359, 13.359774277845295])</answer>", "action": "('move', [-19.592532264594404, 1.562274574170359, 13.359774277845295])", "reward": 0.0, "image": 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"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', [-19.592532264594404, 1.562274574170359, 13.359774277845295])</answer>", "action": "('move', [-19.592532264594404, 1.562274574170359, 13.359774277845295])", "reward": 0.0, "image": 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"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": "2e267fe1a09161ec8327f5c7803ceeb1f9c7ce9e3f371326e45295a34b186b4e"}
{"episode": 79, "episode_seed": 2934594491, "split": "train", "timestamp": "2025-09-15T22:39:30.899280", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2934594491, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [1.093679159413395, -15.498929781679172, 28.776904952420637])</answer>", "action": "('move', [1.093679159413395, -15.498929781679172, 28.776904952420637])", "reward": 0.0, "image": 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"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', [1.093679159413395, -15.498929781679172, 28.776904952420637])</answer>", "action": "('move', [1.093679159413395, -15.498929781679172, 28.776904952420637])", "reward": 0.0, "image": 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"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', [1.093679159413395, -15.498929781679172, 28.776904952420637])</answer>", "action": "('move', [1.093679159413395, -15.498929781679172, 28.776904952420637])", "reward": 0.0, "image": 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"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": "032424e1c3c92df4e528dbe9f2c78119068b28b6a812c7ee7252eadc001dafe7"}
{"episode": 80, "episode_seed": 524150214, "split": "train", "timestamp": "2025-09-15T22:39:31.504555", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 524150214, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-1.6491739616394956, -2.0489083583789998, 33.425251806232346])</answer>", "action": "('move', [-1.6491739616394956, -2.0489083583789998, 33.425251806232346])", "reward": 0.0, "image": 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"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', [-1.6491739616394956, -2.0489083583789998, 33.425251806232346])</answer>", "action": "('move', [-1.6491739616394956, -2.0489083583789998, 33.425251806232346])", "reward": 0.0, "image": 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"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', [-1.6491739616394956, -2.0489083583789998, 33.425251806232346])</answer>", "action": "('move', [-1.6491739616394956, -2.0489083583789998, 33.425251806232346])", "reward": 0.0, "image": 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"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": "235af53ce15adb876aae11e2f1ba28d8a80988c4de0cf0c17140c513aba4aa85"}
{"episode": 81, "episode_seed": 2619915691, "split": "train", "timestamp": "2025-09-15T22:39:32.112597", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2619915691, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-4.478256784540361, 1.1319525014575456, 56.22618677460783])</answer>", "action": "('move', [-4.478256784540361, 1.1319525014575456, 56.22618677460783])", "reward": 0.0, "image": 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"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', [-4.478256784540361, 1.1319525014575456, 56.22618677460783])</answer>", "action": "('move', [-4.478256784540361, 1.1319525014575456, 56.22618677460783])", "reward": 0.0, "image": 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"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', [-4.478256784540361, 1.1319525014575456, 56.22618677460783])</answer>", "action": "('move', [-4.478256784540361, 1.1319525014575456, 56.22618677460783])", "reward": 0.0, "image": 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"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": "621595cf199fafa92637885e231752de9e2e3233ae6739e96bec55f57f307a2b"}
{"episode": 82, "episode_seed": 2126768636, "split": "train", "timestamp": "2025-09-15T22:39:33.072527", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2126768636, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-18.37007027716256, -9.60463084898585, 26.729251645305965])</answer>", "action": "('move', [-18.37007027716256, -9.60463084898585, 26.729251645305965])", "reward": 0.0, "image": 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"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', [-18.37007027716256, -9.60463084898585, 26.729251645305965])</answer>", "action": "('move', [-18.37007027716256, -9.60463084898585, 26.729251645305965])", "reward": 0.0, "image": 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"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', [-18.37007027716256, -9.60463084898585, 26.729251645305965])</answer>", "action": "('move', [-18.37007027716256, -9.60463084898585, 26.729251645305965])", "reward": 0.0, "image": 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"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": "2e88add07bf9353befe9d46c9ac511052d88746e4421dda4cea997b6222550c9"}
{"episode": 83, "episode_seed": 3578544903, "split": "train", "timestamp": "2025-09-15T22:39:33.684565", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3578544903, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [12.760848092386503, -10.230196584173102, -49.93354979193466])</answer>", "action": "('move', [12.760848092386503, -10.230196584173102, -49.93354979193466])", "reward": 0.0, "image": 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"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', [12.760848092386503, -10.230196584173102, -49.93354979193466])</answer>", "action": "('move', [12.760848092386503, -10.230196584173102, -49.93354979193466])", "reward": 0.0, "image": 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"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', [12.760848092386503, -10.230196584173102, -49.93354979193466])</answer>", "action": "('move', [12.760848092386503, -10.230196584173102, -49.93354979193466])", "reward": 0.0, "image": 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"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": "0fbee593f614f475539218a7a74820d1059eb8520251e036ef69102ab33bf8cb"}
{"episode": 84, "episode_seed": 147697582, "split": "train", "timestamp": "2025-09-15T22:39:34.293018", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 147697582, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [2.564960226856813, 6.53227533612382, -4.776139672497749])</answer>", "action": "('move', [2.564960226856813, 6.53227533612382, -4.776139672497749])", "reward": 0.0, "image": 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"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', [2.564960226856813, 6.53227533612382, -4.776139672497749])</answer>", "action": "('move', [2.564960226856813, 6.53227533612382, -4.776139672497749])", "reward": 0.0, "image": 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"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', [2.564960226856813, 6.53227533612382, -4.776139672497749])</answer>", "action": "('move', [2.564960226856813, 6.53227533612382, -4.776139672497749])", "reward": 0.0, "image": 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"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": "b2e76259846cf0915a6c17d43543575b497beac9b3d6f3bd6df5aad7207576c6"}
{"episode": 85, "episode_seed": 744595490, "split": "train", "timestamp": "2025-09-15T22:39:35.261411", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 744595490, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-5.0331233496024685, 6.459724189864821, -50.70511957429817])</answer>", "action": "('move', [-5.0331233496024685, 6.459724189864821, -50.70511957429817])", "reward": 0.0, "image": 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"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', [-5.0331233496024685, 6.459724189864821, -50.70511957429817])</answer>", "action": "('move', [-5.0331233496024685, 6.459724189864821, -50.70511957429817])", "reward": 0.0, "image": 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"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', [-5.0331233496024685, 6.459724189864821, -50.70511957429817])</answer>", "action": "('move', [-5.0331233496024685, 6.459724189864821, -50.70511957429817])", "reward": 0.0, "image": 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"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": "9af2505408b17c5ba783976a6a11bb33953681829a8415d10e8271b35ffabd9a"}
{"episode": 86, "episode_seed": 3905501389, "split": "train", "timestamp": "2025-09-15T22:39:35.871480", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3905501389, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [0.9930892469322697, -1.0230026285555984, -23.369234445833712])</answer>", "action": "('move', [0.9930892469322697, -1.0230026285555984, -23.369234445833712])", "reward": 0.0, "image": 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"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', [0.9930892469322697, -1.0230026285555984, -23.369234445833712])</answer>", "action": "('move', [0.9930892469322697, -1.0230026285555984, -23.369234445833712])", "reward": 0.0, "image": 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"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', [0.9930892469322697, -1.0230026285555984, -23.369234445833712])</answer>", "action": "('move', [0.9930892469322697, -1.0230026285555984, -23.369234445833712])", "reward": 0.0, "image": 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"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": "267e88d39681a545448f9b115c6cd75ce13d0d546bc4973784295a3514878738"}
{"episode": 87, "episode_seed": 1679592528, "split": "train", "timestamp": "2025-09-15T22:39:36.477401", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1679592528, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-2.8630084945701206, 0.2448714873311941, 43.76652028683767])</answer>", "action": "('move', [-2.8630084945701206, 0.2448714873311941, 43.76652028683767])", "reward": 0.0, "image": 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"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', [-2.8630084945701206, 0.2448714873311941, 43.76652028683767])</answer>", "action": "('move', [-2.8630084945701206, 0.2448714873311941, 43.76652028683767])", "reward": 0.0, "image": 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"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', [-2.8630084945701206, 0.2448714873311941, 43.76652028683767])</answer>", "action": "('move', [-2.8630084945701206, 0.2448714873311941, 43.76652028683767])", "reward": 0.0, "image": 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"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": "68e765188e6e1b3a3ef11e979b63a003bf79abc86cb299b9061c8353cae7f309"}
{"episode": 88, "episode_seed": 1111451555, "split": "train", "timestamp": "2025-09-15T22:39:37.079728", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1111451555, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [5.711825969284977, 0.6664603656728321, -2.084145981731391])</answer>", "action": "('move', [5.711825969284977, 0.6664603656728321, -2.084145981731391])", "reward": 0.0, "image": 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"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', [5.711825969284977, 0.6664603656728321, -2.084145981731391])</answer>", "action": "('move', [5.711825969284977, 0.6664603656728321, -2.084145981731391])", "reward": 0.0, "image": 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"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', [5.711825969284977, 0.6664603656728321, -2.084145981731391])</answer>", "action": "('move', [5.711825969284977, 0.6664603656728321, -2.084145981731391])", "reward": 0.0, "image": 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"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": "65750f33555674a126c3544cf3dce0dc81a473f72d1cc9c108e8f26b72f10d2d"}
{"episode": 89, "episode_seed": 782698033, "split": "train", "timestamp": "2025-09-15T22:39:38.036391", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 782698033, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [2.9410834628193476, -10.494526498223133, 49.78677649347427])</answer>", "action": "('move', [2.9410834628193476, -10.494526498223133, 49.78677649347427])", "reward": 0.0, "image": 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"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', [2.9410834628193476, -10.494526498223133, 49.78677649347427])</answer>", "action": "('move', [2.9410834628193476, -10.494526498223133, 49.78677649347427])", "reward": 0.0, "image": 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"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', [2.9410834628193476, -10.494526498223133, 49.78677649347427])</answer>", "action": "('move', [2.9410834628193476, -10.494526498223133, 49.78677649347427])", "reward": 0.0, "image": 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"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": "c2d0d617351b58e0c7a37d57f87ecb8ee5897d1223e1c6f9b8b472e80434e795"}
{"episode": 90, "episode_seed": 2845511527, "split": "train", "timestamp": "2025-09-15T22:39:38.647273", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2845511527, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-2.3315780458375572, -1.4616324761180497, 44.0864812685511])</answer>", "action": "('move', [-2.3315780458375572, -1.4616324761180497, 44.0864812685511])", "reward": 0.0, "image": 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"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', [-2.3315780458375572, -1.4616324761180497, 44.0864812685511])</answer>", "action": "('move', [-2.3315780458375572, -1.4616324761180497, 44.0864812685511])", "reward": 0.0, "image": 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"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', [-2.3315780458375572, -1.4616324761180497, 44.0864812685511])</answer>", "action": "('move', [-2.3315780458375572, -1.4616324761180497, 44.0864812685511])", "reward": 0.0, "image": 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"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": "4ba7b2b7c1989b23ba0f0ff12c71f14bb7cde2ec09a5eb3e5863dccefb2f71f9"}
{"episode": 91, "episode_seed": 3244252547, "split": "train", "timestamp": "2025-09-15T22:39:39.252798", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 3244252547, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [17.083332036225222, -5.1049265802469455, -7.372135388337938])</answer>", "action": "('move', [17.083332036225222, -5.1049265802469455, -7.372135388337938])", "reward": 0.0, "image": 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"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', [17.083332036225222, -5.1049265802469455, -7.372135388337938])</answer>", "action": "('move', [17.083332036225222, -5.1049265802469455, -7.372135388337938])", "reward": 0.0, "image": 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"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', [17.083332036225222, -5.1049265802469455, -7.372135388337938])</answer>", "action": "('move', [17.083332036225222, -5.1049265802469455, -7.372135388337938])", "reward": 0.0, "image": 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"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": "f84312d4dd251aff1cae53d1c360ff8dcfa2b99480b4b740ac153ec99eeea5be"}
{"episode": 92, "episode_seed": 1338788865, "split": "train", "timestamp": "2025-09-15T22:39:40.204078", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1338788865, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-5.925564921358725, -2.0303442781320613, -16.905165140345957])</answer>", "action": "('move', [-5.925564921358725, -2.0303442781320613, -16.905165140345957])", "reward": 0.0, "image": 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"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', [-5.925564921358725, -2.0303442781320613, -16.905165140345957])</answer>", "action": "('move', [-5.925564921358725, -2.0303442781320613, -16.905165140345957])", "reward": 0.0, "image": 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"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', [-5.925564921358725, -2.0303442781320613, -16.905165140345957])</answer>", "action": "('move', [-5.925564921358725, -2.0303442781320613, -16.905165140345957])", "reward": 0.0, "image": 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"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": "ee691548afb5468ce314936a1a038bdfa2080e86df96aab3427014a6ad8f979e"}
{"episode": 93, "episode_seed": 1826030589, "split": "train", "timestamp": "2025-09-15T22:39:40.814512", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 1826030589, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-63.25776784412117, -34.503451738715356, 10.615581549915968])</answer>", "action": "('move', [-63.25776784412117, -34.503451738715356, 10.615581549915968])", "reward": 0.0, "image": 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"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', [-63.25776784412117, -34.503451738715356, 10.615581549915968])</answer>", "action": "('move', [-63.25776784412117, -34.503451738715356, 10.615581549915968])", "reward": 0.0, "image": 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"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', [-63.25776784412117, -34.503451738715356, 10.615581549915968])</answer>", "action": "('move', [-63.25776784412117, -34.503451738715356, 10.615581549915968])", "reward": 0.0, "image": 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"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": "69f1567b211c8de35e134360b224ef10b1466015cad8879d0d7819be52c0a91b"}
{"episode": 94, "episode_seed": 2233675141, "split": "train", "timestamp": "2025-09-15T22:39:41.424902", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2233675141, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [3.1394558507579444, 0.8294116102360073, 53.73575883090564])</answer>", "action": "('move', [3.1394558507579444, 0.8294116102360073, 53.73575883090564])", "reward": 0.0, "image": 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"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', [3.1394558507579444, 0.8294116102360073, 53.73575883090564])</answer>", "action": "('move', [3.1394558507579444, 0.8294116102360073, 53.73575883090564])", "reward": 0.0, "image": 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"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', [3.1394558507579444, 0.8294116102360073, 53.73575883090564])</answer>", "action": "('move', [3.1394558507579444, 0.8294116102360073, 53.73575883090564])", "reward": 0.0, "image": 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"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": "a1cfc2937fd2f16b8d673c6b60d992ca613ff930a9e6c9ede7a877206c674239"}
{"episode": 95, "episode_seed": 893102645, "split": "train", "timestamp": "2025-09-15T22:39:42.026860", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 893102645, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [5.187771668649639, -0.04011708935782791, -26.766984874911532])</answer>", "action": "('move', [5.187771668649639, -0.04011708935782791, -26.766984874911532])", "reward": 0.0, "image": 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"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', [5.187771668649639, -0.04011708935782791, -26.766984874911532])</answer>", "action": "('move', [5.187771668649639, -0.04011708935782791, -26.766984874911532])", "reward": 0.0, "image": 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"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', [5.187771668649639, -0.04011708935782791, -26.766984874911532])</answer>", "action": "('move', [5.187771668649639, -0.04011708935782791, -26.766984874911532])", "reward": 0.0, "image": 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"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": "a9fcf6bbac1663b6236b8bf1cf1567ef6ad25d80a9e331ec98482a868d35d8d1"}
{"episode": 96, "episode_seed": 2348102761, "split": "train", "timestamp": "2025-09-15T22:39:43.017450", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2348102761, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-53.150188428476945, -11.539304926438534, -45.80251158028202])</answer>", "action": "('move', [-53.150188428476945, -11.539304926438534, -45.80251158028202])", "reward": 0.0, "image": 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"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', [-53.150188428476945, -11.539304926438534, -45.80251158028202])</answer>", "action": "('move', [-53.150188428476945, -11.539304926438534, -45.80251158028202])", "reward": 0.0, "image": 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"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', [-53.150188428476945, -11.539304926438534, -45.80251158028202])</answer>", "action": "('move', [-53.150188428476945, -11.539304926438534, -45.80251158028202])", "reward": 0.0, "image": 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"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": "14d699ff7c9b27e7d37e37ed19dcd8511d5b6cabca03266b3423489b2251ff30"}
{"episode": 97, "episode_seed": 2438254339, "split": "train", "timestamp": "2025-09-15T22:39:43.625718", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 2438254339, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [2.720941269316961, -3.398912460731589, -16.313753120301907])</answer>", "action": "('move', [2.720941269316961, -3.398912460731589, -16.313753120301907])", "reward": 0.0, "image": 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"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', [2.720941269316961, -3.398912460731589, -16.313753120301907])</answer>", "action": "('move', [2.720941269316961, -3.398912460731589, -16.313753120301907])", "reward": 0.0, "image": 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"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', [2.720941269316961, -3.398912460731589, -16.313753120301907])</answer>", "action": "('move', [2.720941269316961, -3.398912460731589, -16.313753120301907])", "reward": 0.0, "image": 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"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": "941669b362264df459377802a30f1e71e8aad69e6006ec2d148a77cae23ea1ba"}
{"episode": 98, "episode_seed": 793943861, "split": "train", "timestamp": "2025-09-15T22:39:44.230793", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 793943861, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [-5.5997532863843205, 12.937013809817975, -22.432591366823203])</answer>", "action": "('move', [-5.5997532863843205, 12.937013809817975, -22.432591366823203])", "reward": 0.0, "image": 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"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', [-5.5997532863843205, 12.937013809817975, -22.432591366823203])</answer>", "action": "('move', [-5.5997532863843205, 12.937013809817975, -22.432591366823203])", "reward": 0.0, "image": 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"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', [-5.5997532863843205, 12.937013809817975, -22.432591366823203])</answer>", "action": "('move', [-5.5997532863843205, 12.937013809817975, -22.432591366823203])", "reward": 0.0, "image": 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"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": "b2e9924ab5fd9737f5347f8f4ccfcc9c8a3168c63ae661b7bb65fcb1708e897d"}
{"episode": 99, "episode_seed": 134489564, "split": "train", "timestamp": "2025-09-15T22:39:45.188790", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db96e4f1f0>", "max_steps": 10, "abs_size": 336, "master_seed": 42, "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": 134489564, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 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', [18.41371930163906, 17.836360582662536, 12.324030615966706])</answer>", "action": "('move', [18.41371930163906, 17.836360582662536, 12.324030615966706])", "reward": 0.0, "image": 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"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', [18.41371930163906, 17.836360582662536, 12.324030615966706])</answer>", "action": "('move', [18.41371930163906, 17.836360582662536, 12.324030615966706])", "reward": 0.0, "image": 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"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', [18.41371930163906, 17.836360582662536, 12.324030615966706])</answer>", "action": "('move', [18.41371930163906, 17.836360582662536, 12.324030615966706])", "reward": 0.0, "image": 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"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": "62453c9de6d61a9e160c046c017f64047d7b072ea32d683d21775db1dfbd7f19"}