diff --git "a/train/batch_000654.jsonl" "b/train/batch_000654.jsonl"
new file mode 100644--- /dev/null
+++ "b/train/batch_000654.jsonl"
@@ -0,0 +1,100 @@
+{"episode": 65400, "episode_seed": 3397229503, "split": "train", "timestamp": "2025-09-15T23:56:43.214538", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3397229503, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [2.205210580734446, -6.456235835616478, -16.432902424048148])</answer>", "action": "('move', [2.205210580734446, -6.456235835616478, -16.432902424048148])", "reward": 0.0, "image": 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"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', [2.205210580734446, -6.456235835616478, -16.432902424048148])</answer>", "action": "('move', [2.205210580734446, -6.456235835616478, -16.432902424048148])", "reward": 0.0, "image": 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"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', [2.205210580734446, -6.456235835616478, -16.432902424048148])</answer>", "action": "('move', [2.205210580734446, -6.456235835616478, -16.432902424048148])", "reward": 0.0, "image": 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"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": "8d47460542fad4a921e26c6f3bbc3ddd3788017804955400a95f06db79c649ab"}
+{"episode": 65401, "episode_seed": 1017539879, "split": "train", "timestamp": "2025-09-15T23:56:43.823367", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1017539879, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-8.85346189766219, 4.204041462084198, -45.363076804033625])</answer>", "action": "('move', [-8.85346189766219, 4.204041462084198, -45.363076804033625])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-8.85346189766219, 4.204041462084198, -45.363076804033625])</answer>", "action": "('move', [-8.85346189766219, 4.204041462084198, -45.363076804033625])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-8.85346189766219, 4.204041462084198, -45.363076804033625])</answer>", "action": "('move', [-8.85346189766219, 4.204041462084198, -45.363076804033625])", "reward": 0.0, "image": 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"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": "38e79fda1c684fdb0522f0788e7322c3811035dae554556164a03954e6946cfa"}
+{"episode": 65402, "episode_seed": 2470607042, "split": "train", "timestamp": "2025-09-15T23:56:44.776992", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2470607042, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-0.2661472242837895, -0.2720012275103603, 52.12428160300374])</answer>", "action": "('move', [-0.2661472242837895, -0.2720012275103603, 52.12428160300374])", "reward": 0.0, "image": 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"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', [-0.2661472242837895, -0.2720012275103603, 52.12428160300374])</answer>", "action": "('move', [-0.2661472242837895, -0.2720012275103603, 52.12428160300374])", "reward": 0.0, "image": 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"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', [-0.2661472242837895, -0.2720012275103603, 52.12428160300374])</answer>", "action": "('move', [-0.2661472242837895, -0.2720012275103603, 52.12428160300374])", "reward": 0.0, "image": 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"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": "57c83649605a5953fc1ef92cf21cff5fefbc60db66befc5830e6debb770d08bd"}
+{"episode": 65403, "episode_seed": 119424226, "split": "train", "timestamp": "2025-09-15T23:56:45.384903", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 119424226, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [5.387487185109911, -12.087341786260867, -58.46658705464237])</answer>", "action": "('move', [5.387487185109911, -12.087341786260867, -58.46658705464237])", "reward": 0.0, "image": 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"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', [5.387487185109911, -12.087341786260867, -58.46658705464237])</answer>", "action": "('move', [5.387487185109911, -12.087341786260867, -58.46658705464237])", "reward": 0.0, "image": 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"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', [5.387487185109911, -12.087341786260867, -58.46658705464237])</answer>", "action": "('move', [5.387487185109911, -12.087341786260867, -58.46658705464237])", "reward": 0.0, "image": 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"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": "3c2bc3ebfc6fabfc9c9b95df671259fed1a81c33ce1a7b893ba3e5a7ade45f79"}
+{"episode": 65404, "episode_seed": 488495326, "split": "train", "timestamp": "2025-09-15T23:56:45.985201", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 488495326, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [10.973177330537242, -0.47793536317080476, 26.37440571783056])</answer>", "action": "('move', [10.973177330537242, -0.47793536317080476, 26.37440571783056])", "reward": 0.0, "image": 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"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', [10.973177330537242, -0.47793536317080476, 26.37440571783056])</answer>", "action": "('move', [10.973177330537242, -0.47793536317080476, 26.37440571783056])", "reward": 0.0, "image": 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"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', [10.973177330537242, -0.47793536317080476, 26.37440571783056])</answer>", "action": "('move', [10.973177330537242, -0.47793536317080476, 26.37440571783056])", "reward": 0.0, "image": 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"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": "e2d45da05f3675f346ac69409400dc6ce166c2c05a096916615daeb9bda3b85e"}
+{"episode": 65405, "episode_seed": 1140242067, "split": "train", "timestamp": "2025-09-15T23:56:46.922800", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1140242067, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-17.948423117643404, -8.087946842753228, -35.849979476521014])</answer>", "action": "('move', [-17.948423117643404, -8.087946842753228, -35.849979476521014])", "reward": 0.0, "image": 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"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', [-17.948423117643404, -8.087946842753228, -35.849979476521014])</answer>", "action": "('move', [-17.948423117643404, -8.087946842753228, -35.849979476521014])", "reward": 0.0, "image": 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"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', [-17.948423117643404, -8.087946842753228, -35.849979476521014])</answer>", "action": "('move', [-17.948423117643404, -8.087946842753228, -35.849979476521014])", "reward": 0.0, "image": 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"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": "d81b309ff71705bd07a3f14c87886bc46e0f222560febd969941a1e2716a2f3e"}
+{"episode": 65406, "episode_seed": 378748528, "split": "train", "timestamp": "2025-09-15T23:56:47.528236", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 378748528, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [2.5275324640621, 0.008936937285031352, 0.25012426050113845])</answer>", "action": "('move', [2.5275324640621, 0.008936937285031352, 0.25012426050113845])", "reward": 0.0, "image": 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"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', [2.5275324640621, 0.008936937285031352, 0.25012426050113845])</answer>", "action": "('move', [2.5275324640621, 0.008936937285031352, 0.25012426050113845])", "reward": 0.0, "image": 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"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', [2.5275324640621, 0.008936937285031352, 0.25012426050113845])</answer>", "action": "('move', [2.5275324640621, 0.008936937285031352, 0.25012426050113845])", "reward": 0.0, "image": 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"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": "017108a6f4b5b2b2d50aff0247711e45137834f62910945c8dc3f96f044df9f0"}
+{"episode": 65407, "episode_seed": 3912496551, "split": "train", "timestamp": "2025-09-15T23:56:48.131530", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3912496551, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-3.8187698461499973, 3.2592122305217175, -41.66261804480157])</answer>", "action": "('move', [-3.8187698461499973, 3.2592122305217175, -41.66261804480157])", "reward": 0.0, "image": 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"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', [-3.8187698461499973, 3.2592122305217175, -41.66261804480157])</answer>", "action": "('move', [-3.8187698461499973, 3.2592122305217175, -41.66261804480157])", "reward": 0.0, "image": 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"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', [-3.8187698461499973, 3.2592122305217175, -41.66261804480157])</answer>", "action": "('move', [-3.8187698461499973, 3.2592122305217175, -41.66261804480157])", "reward": 0.0, "image": 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"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": "37c440eef6e206568e35c96582506393dbe328b9ec3796d929dae4e25b675f1e"}
+{"episode": 65408, "episode_seed": 3380112409, "split": "train", "timestamp": "2025-09-15T23:56:48.737181", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3380112409, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-13.939485155141071, 13.601989557101952, -55.83241280942406])</answer>", "action": "('move', [-13.939485155141071, 13.601989557101952, -55.83241280942406])", "reward": 0.0, "image": 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"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', [-13.939485155141071, 13.601989557101952, -55.83241280942406])</answer>", "action": "('move', [-13.939485155141071, 13.601989557101952, -55.83241280942406])", "reward": 0.0, "image": 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"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', [-13.939485155141071, 13.601989557101952, -55.83241280942406])</answer>", "action": "('move', [-13.939485155141071, 13.601989557101952, -55.83241280942406])", "reward": 0.0, "image": 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"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": "0f4d70d67cf7df648c999bacb2a72f4baca6744292faa25296f1c732aa0e4f14"}
+{"episode": 65409, "episode_seed": 3860478794, "split": "train", "timestamp": "2025-09-15T23:56:49.675754", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3860478794, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-5.38871070196797, 1.1088759846905296, 50.77102944278929])</answer>", "action": "('move', [-5.38871070196797, 1.1088759846905296, 50.77102944278929])", "reward": 0.0, "image": 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"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', [-5.38871070196797, 1.1088759846905296, 50.77102944278929])</answer>", "action": "('move', [-5.38871070196797, 1.1088759846905296, 50.77102944278929])", "reward": 0.0, "image": 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"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', [-5.38871070196797, 1.1088759846905296, 50.77102944278929])</answer>", "action": "('move', [-5.38871070196797, 1.1088759846905296, 50.77102944278929])", "reward": 0.0, "image": 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"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": "106b2b16b91f6fb9fd5fbd2379aba4ef7c4ddf19e2804f15908a54a288c2549e"}
+{"episode": 65410, "episode_seed": 2431901263, "split": "train", "timestamp": "2025-09-15T23:56:50.282348", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2431901263, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [3.7255215106003026, -1.294667201166847, -46.80596682370413])</answer>", "action": "('move', [3.7255215106003026, -1.294667201166847, -46.80596682370413])", "reward": 0.0, "image": 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"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', [3.7255215106003026, -1.294667201166847, -46.80596682370413])</answer>", "action": "('move', [3.7255215106003026, -1.294667201166847, -46.80596682370413])", "reward": 0.0, "image": 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"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', [3.7255215106003026, -1.294667201166847, -46.80596682370413])</answer>", "action": "('move', [3.7255215106003026, -1.294667201166847, -46.80596682370413])", "reward": 0.0, "image": 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"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": "c2bea4601116b2109b971ce1e739d3a5da4414a5782fc8428b41e841aa59e38b"}
+{"episode": 65411, "episode_seed": 1016727774, "split": "train", "timestamp": "2025-09-15T23:56:50.897987", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1016727774, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-110.40538914990132, -53.94102170227498, 27.84604604144359])</answer>", "action": "('move', [-110.40538914990132, -53.94102170227498, 27.84604604144359])", "reward": 0.0, "image": 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"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', [-110.40538914990132, -53.94102170227498, 27.84604604144359])</answer>", "action": "('move', [-110.40538914990132, -53.94102170227498, 27.84604604144359])", "reward": 0.0, "image": 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"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', [-110.40538914990132, -53.94102170227498, 27.84604604144359])</answer>", "action": "('move', [-110.40538914990132, -53.94102170227498, 27.84604604144359])", "reward": 0.0, "image": 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"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": "b78cf2903408e229ffc36ffa353f07730c5ebd32a2add8e5bd73c921e0c4142f"}
+{"episode": 65412, "episode_seed": 3873341662, "split": "train", "timestamp": "2025-09-15T23:56:51.869847", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3873341662, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [44.26829476727804, 74.75073900524872, 33.998018325359475])</answer>", "action": "('move', [44.26829476727804, 74.75073900524872, 33.998018325359475])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [44.26829476727804, 74.75073900524872, 33.998018325359475])</answer>", "action": "('move', [44.26829476727804, 74.75073900524872, 33.998018325359475])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [44.26829476727804, 74.75073900524872, 33.998018325359475])</answer>", "action": "('move', [44.26829476727804, 74.75073900524872, 33.998018325359475])", "reward": 0.0, "image": 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"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": "0a94981e7d647a66e2246e12c183eab3e583e26479d980bc3f6168a00137cad6"}
+{"episode": 65413, "episode_seed": 4073162138, "split": "train", "timestamp": "2025-09-15T23:56:52.484241", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 4073162138, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-65.36106184001052, 61.71785984255624, -10.681060468936332])</answer>", "action": "('move', [-65.36106184001052, 61.71785984255624, -10.681060468936332])", "reward": 0.0, "image": 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"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', [-65.36106184001052, 61.71785984255624, -10.681060468936332])</answer>", "action": "('move', [-65.36106184001052, 61.71785984255624, -10.681060468936332])", "reward": 0.0, "image": 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"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', [-65.36106184001052, 61.71785984255624, -10.681060468936332])</answer>", "action": "('move', [-65.36106184001052, 61.71785984255624, -10.681060468936332])", "reward": 0.0, "image": 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"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": "9dfa087466d9f3e2a5a5531e829e6ce500d55859fad667191670e68e77ba6bad"}
+{"episode": 65414, "episode_seed": 1414094102, "split": "train", "timestamp": "2025-09-15T23:56:53.097741", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1414094102, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [1.2009617177840404, -1.5274478582961377, -49.509284873325704])</answer>", "action": "('move', [1.2009617177840404, -1.5274478582961377, -49.509284873325704])", "reward": 0.0, "image": 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"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', [1.2009617177840404, -1.5274478582961377, -49.509284873325704])</answer>", "action": "('move', [1.2009617177840404, -1.5274478582961377, -49.509284873325704])", "reward": 0.0, "image": 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"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', [1.2009617177840404, -1.5274478582961377, -49.509284873325704])</answer>", "action": "('move', [1.2009617177840404, -1.5274478582961377, -49.509284873325704])", "reward": 0.0, "image": 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"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": "0f567af1c42f8a7d409ad7362474c3a516b9dfdee23be35bf5871fdc02c0b87f"}
+{"episode": 65415, "episode_seed": 1838689790, "split": "train", "timestamp": "2025-09-15T23:56:53.715891", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1838689790, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [24.86662069616777, -11.833804303677967, -37.438463444436394])</answer>", "action": "('move', [24.86662069616777, -11.833804303677967, -37.438463444436394])", "reward": 0.0, "image": 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"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', [24.86662069616777, -11.833804303677967, -37.438463444436394])</answer>", "action": "('move', [24.86662069616777, -11.833804303677967, -37.438463444436394])", "reward": 0.0, "image": 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"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', [24.86662069616777, -11.833804303677967, -37.438463444436394])</answer>", "action": "('move', [24.86662069616777, -11.833804303677967, -37.438463444436394])", "reward": 0.0, "image": 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"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": "79d93f4109203b059bcbda7837ed0ab1c660c0143eec47d19b12756aaef9127d"}
+{"episode": 65416, "episode_seed": 2607122643, "split": "train", "timestamp": "2025-09-15T23:56:54.681491", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2607122643, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-18.664911548288007, 9.278236261150694, -20.2728266660463])</answer>", "action": "('move', [-18.664911548288007, 9.278236261150694, -20.2728266660463])", "reward": 0.0, "image": 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"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', [-18.664911548288007, 9.278236261150694, -20.2728266660463])</answer>", "action": "('move', [-18.664911548288007, 9.278236261150694, -20.2728266660463])", "reward": 0.0, "image": 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"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', [-18.664911548288007, 9.278236261150694, -20.2728266660463])</answer>", "action": "('move', [-18.664911548288007, 9.278236261150694, -20.2728266660463])", "reward": 0.0, "image": 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"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": "d7bd5e5dec55f00a83bec529a7e3055e31c8faf67a364c92d9cc1d5ae6605736"}
+{"episode": 65417, "episode_seed": 741331825, "split": "train", "timestamp": "2025-09-15T23:56:55.294536", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 741331825, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [9.60071488074828, -7.558028806604107, 18.283891903169888])</answer>", "action": "('move', [9.60071488074828, -7.558028806604107, 18.283891903169888])", "reward": 0.0, "image": 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"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', [9.60071488074828, -7.558028806604107, 18.283891903169888])</answer>", "action": "('move', [9.60071488074828, -7.558028806604107, 18.283891903169888])", "reward": 0.0, "image": 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"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', [9.60071488074828, -7.558028806604107, 18.283891903169888])</answer>", "action": "('move', [9.60071488074828, -7.558028806604107, 18.283891903169888])", "reward": 0.0, "image": 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"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": "55b33f2c43028c1797cc6abaeac4b5c91591c66a3e64afe2a0867a266a0f2e91"}
+{"episode": 65418, "episode_seed": 1675912831, "split": "train", "timestamp": "2025-09-15T23:56:55.899524", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1675912831, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [29.15261589532661, 35.83088545861167, -23.82090231127347])</answer>", "action": "('move', [29.15261589532661, 35.83088545861167, -23.82090231127347])", "reward": 0.0, "image": 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"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', [29.15261589532661, 35.83088545861167, -23.82090231127347])</answer>", "action": "('move', [29.15261589532661, 35.83088545861167, -23.82090231127347])", "reward": 0.0, "image": 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"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', [29.15261589532661, 35.83088545861167, -23.82090231127347])</answer>", "action": "('move', [29.15261589532661, 35.83088545861167, -23.82090231127347])", "reward": 0.0, "image": 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"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": "662201d75fb79ac12ba16cde97d3171118da904cbbd9b12d480db8592c45c33e"}
+{"episode": 65419, "episode_seed": 125466459, "split": "train", "timestamp": "2025-09-15T23:56:56.841830", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 125466459, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [1.8878902782245193, -3.2419200465446347, 44.534391732552514])</answer>", "action": "('move', [1.8878902782245193, -3.2419200465446347, 44.534391732552514])", "reward": 0.0, "image": 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"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', [1.8878902782245193, -3.2419200465446347, 44.534391732552514])</answer>", "action": "('move', [1.8878902782245193, -3.2419200465446347, 44.534391732552514])", "reward": 0.0, "image": 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"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', [1.8878902782245193, -3.2419200465446347, 44.534391732552514])</answer>", "action": "('move', [1.8878902782245193, -3.2419200465446347, 44.534391732552514])", "reward": 0.0, "image": 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"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": "ce5b6644ba98ee41b9f1777fab626856ea049702e1140dfb1abf65aa93354d58"}
+{"episode": 65420, "episode_seed": 1761441028, "split": "train", "timestamp": "2025-09-15T23:56:57.447088", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1761441028, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [2.675545538901611, -4.2493729415249915, -5.672555222124108])</answer>", "action": "('move', [2.675545538901611, -4.2493729415249915, -5.672555222124108])", "reward": 0.0, "image": 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"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', [2.675545538901611, -4.2493729415249915, -5.672555222124108])</answer>", "action": "('move', [2.675545538901611, -4.2493729415249915, -5.672555222124108])", "reward": 0.0, "image": 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"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', [2.675545538901611, -4.2493729415249915, -5.672555222124108])</answer>", "action": "('move', [2.675545538901611, -4.2493729415249915, -5.672555222124108])", "reward": 0.0, "image": 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"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": "04d6b71280af40a8fea65f2c3d08ecefcf26978ff111adcc2d580b35a4105870"}
+{"episode": 65421, "episode_seed": 776254104, "split": "train", "timestamp": "2025-09-15T23:56:58.051580", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 776254104, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-13.383622081418201, -2.929174931962447, -31.388534599575905])</answer>", "action": "('move', [-13.383622081418201, -2.929174931962447, -31.388534599575905])", "reward": 0.0, "image": 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"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', [-13.383622081418201, -2.929174931962447, -31.388534599575905])</answer>", "action": "('move', [-13.383622081418201, -2.929174931962447, -31.388534599575905])", "reward": 0.0, "image": 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"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', [-13.383622081418201, -2.929174931962447, -31.388534599575905])</answer>", "action": "('move', [-13.383622081418201, -2.929174931962447, -31.388534599575905])", "reward": 0.0, "image": 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"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": "19cdae7d6ad8eefdfe50897e4f5eabea7fb1d2a333e7babd97cd230bf6344a07"}
+{"episode": 65422, "episode_seed": 1424903086, "split": "train", "timestamp": "2025-09-15T23:56:58.657932", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1424903086, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [1.1854275780072145, -1.4749138066961758, -12.049435008142638])</answer>", "action": "('move', [1.1854275780072145, -1.4749138066961758, -12.049435008142638])", "reward": 0.0, "image": 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"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', [1.1854275780072145, -1.4749138066961758, -12.049435008142638])</answer>", "action": "('move', [1.1854275780072145, -1.4749138066961758, -12.049435008142638])", "reward": 0.0, "image": 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"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', [1.1854275780072145, -1.4749138066961758, -12.049435008142638])</answer>", "action": "('move', [1.1854275780072145, -1.4749138066961758, -12.049435008142638])", "reward": 0.0, "image": 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"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": "474d5f341995f4b6b4dbc5d269b046fbba535e429645acccc50657cde300d978"}
+{"episode": 65423, "episode_seed": 2233314017, "split": "train", "timestamp": "2025-09-15T23:56:59.601276", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2233314017, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-7.95904281256234, -2.949924577006929, 28.482930423158148])</answer>", "action": "('move', [-7.95904281256234, -2.949924577006929, 28.482930423158148])", "reward": 0.0, "image": 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"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', [-7.95904281256234, -2.949924577006929, 28.482930423158148])</answer>", "action": "('move', [-7.95904281256234, -2.949924577006929, 28.482930423158148])", "reward": 0.0, "image": 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"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', [-7.95904281256234, -2.949924577006929, 28.482930423158148])</answer>", "action": "('move', [-7.95904281256234, -2.949924577006929, 28.482930423158148])", "reward": 0.0, "image": 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"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": "04d6e791452f27fad3449e61dfc1938f0c5a1231d39b399d23274df9eef0d2a6"}
+{"episode": 65424, "episode_seed": 4058668508, "split": "train", "timestamp": "2025-09-15T23:57:00.206361", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 4058668508, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [1.2620597881503326, 0.8354976279963845, -4.182519178613082])</answer>", "action": "('move', [1.2620597881503326, 0.8354976279963845, -4.182519178613082])", "reward": 0.0, "image": 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"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', [1.2620597881503326, 0.8354976279963845, -4.182519178613082])</answer>", "action": "('move', [1.2620597881503326, 0.8354976279963845, -4.182519178613082])", "reward": 0.0, "image": 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"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', [1.2620597881503326, 0.8354976279963845, -4.182519178613082])</answer>", "action": "('move', [1.2620597881503326, 0.8354976279963845, -4.182519178613082])", "reward": 0.0, "image": 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"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": "05f79815a17f073be28ad38b6dbc3abdcbdd3bc7abb9786242cdba408016da85"}
+{"episode": 65425, "episode_seed": 2333304432, "split": "train", "timestamp": "2025-09-15T23:57:00.813731", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2333304432, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-2.348934319719796, 1.9861588092570115, 23.040215087497103])</answer>", "action": "('move', [-2.348934319719796, 1.9861588092570115, 23.040215087497103])", "reward": 0.0, "image": 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"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', [-2.348934319719796, 1.9861588092570115, 23.040215087497103])</answer>", "action": "('move', [-2.348934319719796, 1.9861588092570115, 23.040215087497103])", "reward": 0.0, "image": 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"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', [-2.348934319719796, 1.9861588092570115, 23.040215087497103])</answer>", "action": "('move', [-2.348934319719796, 1.9861588092570115, 23.040215087497103])", "reward": 0.0, "image": 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"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": "890d8280e5bcda32950d704bb138a72bcdde947740150a96a8722a1f1124eda7"}
+{"episode": 65426, "episode_seed": 2565462713, "split": "train", "timestamp": "2025-09-15T23:57:01.415835", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2565462713, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-17.912921558393993, -5.744558028889652, -2.545093231133715])</answer>", "action": "('move', [-17.912921558393993, -5.744558028889652, -2.545093231133715])", "reward": 0.0, "image": 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"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', [-17.912921558393993, -5.744558028889652, -2.545093231133715])</answer>", "action": "('move', [-17.912921558393993, -5.744558028889652, -2.545093231133715])", "reward": 0.0, "image": 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"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', [-17.912921558393993, -5.744558028889652, -2.545093231133715])</answer>", "action": "('move', [-17.912921558393993, -5.744558028889652, -2.545093231133715])", "reward": 0.0, "image": 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"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": "ac5bb16707e3887c8587fc1a789cff30421b946231ffe1ab71bab098d873705b"}
+{"episode": 65427, "episode_seed": 1653527264, "split": "train", "timestamp": "2025-09-15T23:57:02.352019", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1653527264, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [2.584928423878679, 3.579717085642732, 12.860545720193423])</answer>", "action": "('move', [2.584928423878679, 3.579717085642732, 12.860545720193423])", "reward": 0.0, "image": 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"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', [2.584928423878679, 3.579717085642732, 12.860545720193423])</answer>", "action": "('move', [2.584928423878679, 3.579717085642732, 12.860545720193423])", "reward": 0.0, "image": 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"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', [2.584928423878679, 3.579717085642732, 12.860545720193423])</answer>", "action": "('move', [2.584928423878679, 3.579717085642732, 12.860545720193423])", "reward": 0.0, "image": 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"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": "9d14e3541633505c95c9a1f66f21c411a38c7c0d865f93e41a64f0bd32c288ac"}
+{"episode": 65428, "episode_seed": 2748777352, "split": "train", "timestamp": "2025-09-15T23:57:02.972464", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2748777352, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-0.4750487146822974, 1.8687908481360516, -56.129482029695964])</answer>", "action": "('move', [-0.4750487146822974, 1.8687908481360516, -56.129482029695964])", "reward": 0.0, "image": 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"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', [-0.4750487146822974, 1.8687908481360516, -56.129482029695964])</answer>", "action": "('move', [-0.4750487146822974, 1.8687908481360516, -56.129482029695964])", "reward": 0.0, "image": 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"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', [-0.4750487146822974, 1.8687908481360516, -56.129482029695964])</answer>", "action": "('move', [-0.4750487146822974, 1.8687908481360516, -56.129482029695964])", "reward": 0.0, "image": 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"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": "83996dd09d50969396bc2cabb4b4ea41fc00e1ff6d8cc23655a7b9a98b9e115e"}
+{"episode": 65429, "episode_seed": 2356009509, "split": "train", "timestamp": "2025-09-15T23:57:03.580727", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2356009509, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-7.127393266555032, 5.7067788779431785, 53.17609222127538])</answer>", "action": "('move', [-7.127393266555032, 5.7067788779431785, 53.17609222127538])", "reward": 0.0, "image": 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"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', [-7.127393266555032, 5.7067788779431785, 53.17609222127538])</answer>", "action": "('move', [-7.127393266555032, 5.7067788779431785, 53.17609222127538])", "reward": 0.0, "image": 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"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', [-7.127393266555032, 5.7067788779431785, 53.17609222127538])</answer>", "action": "('move', [-7.127393266555032, 5.7067788779431785, 53.17609222127538])", "reward": 0.0, "image": 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"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": "df642b59d94ea375e8e9b4d198b250c1d2758228cefd53db5fdcab06f6c16826"}
+{"episode": 65430, "episode_seed": 1395164959, "split": "train", "timestamp": "2025-09-15T23:57:04.564220", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1395164959, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-2.7689337742795934, 1.1757268002977301, 25.33980583120022])</answer>", "action": "('move', [-2.7689337742795934, 1.1757268002977301, 25.33980583120022])", "reward": 0.0, "image": 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"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', [-2.7689337742795934, 1.1757268002977301, 25.33980583120022])</answer>", "action": "('move', [-2.7689337742795934, 1.1757268002977301, 25.33980583120022])", "reward": 0.0, "image": 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"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', [-2.7689337742795934, 1.1757268002977301, 25.33980583120022])</answer>", "action": "('move', [-2.7689337742795934, 1.1757268002977301, 25.33980583120022])", "reward": 0.0, "image": 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"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": "b03c7b3aeedc651f82dcf660a89e9485fd75f0920c71e59dac2d0468183a52d5"}
+{"episode": 65431, "episode_seed": 2989969133, "split": "train", "timestamp": "2025-09-15T23:57:05.170713", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2989969133, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [1.5041627139196736, -0.11108395000401078, -45.436360672513956])</answer>", "action": "('move', [1.5041627139196736, -0.11108395000401078, -45.436360672513956])", "reward": 0.0, "image": 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"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', [1.5041627139196736, -0.11108395000401078, -45.436360672513956])</answer>", "action": "('move', [1.5041627139196736, -0.11108395000401078, -45.436360672513956])", "reward": 0.0, "image": 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"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', [1.5041627139196736, -0.11108395000401078, -45.436360672513956])</answer>", "action": "('move', [1.5041627139196736, -0.11108395000401078, -45.436360672513956])", "reward": 0.0, "image": 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"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": "6ab9919332c8fd45a5a99adb68385f3b1f3a7dcca8babc27b3689ea489641d25"}
+{"episode": 65432, "episode_seed": 210206767, "split": "train", "timestamp": "2025-09-15T23:57:05.773052", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 210206767, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-6.607317436329957, -1.0097398488976268, -23.257714928182466])</answer>", "action": "('move', [-6.607317436329957, -1.0097398488976268, -23.257714928182466])", "reward": 0.0, "image": 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"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', [-6.607317436329957, -1.0097398488976268, -23.257714928182466])</answer>", "action": "('move', [-6.607317436329957, -1.0097398488976268, -23.257714928182466])", "reward": 0.0, "image": 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"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', [-6.607317436329957, -1.0097398488976268, -23.257714928182466])</answer>", "action": "('move', [-6.607317436329957, -1.0097398488976268, -23.257714928182466])", "reward": 0.0, "image": 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"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": "0410db88e3166011680427657091e7271447114d6736c0e1e8fa0e5fa2c4987c"}
+{"episode": 65433, "episode_seed": 1945744354, "split": "train", "timestamp": "2025-09-15T23:57:06.376867", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1945744354, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-14.054999339922148, -4.714564693641322, 2.559712954217597])</answer>", "action": "('move', [-14.054999339922148, -4.714564693641322, 2.559712954217597])", "reward": 0.0, "image": 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"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', [-14.054999339922148, -4.714564693641322, 2.559712954217597])</answer>", "action": "('move', [-14.054999339922148, -4.714564693641322, 2.559712954217597])", "reward": 0.0, "image": 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"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', [-14.054999339922148, -4.714564693641322, 2.559712954217597])</answer>", "action": "('move', [-14.054999339922148, -4.714564693641322, 2.559712954217597])", "reward": 0.0, "image": 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"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": "d11c51b5e84115b7053ca5586b564848628935b3251a4153d0fa23bf90273502"}
+{"episode": 65434, "episode_seed": 1023169312, "split": "train", "timestamp": "2025-09-15T23:57:07.315450", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1023169312, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-3.1001514721830064, -2.271073789623722, -19.308306868493975])</answer>", "action": "('move', [-3.1001514721830064, -2.271073789623722, -19.308306868493975])", "reward": 0.0, "image": 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"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', [-3.1001514721830064, -2.271073789623722, -19.308306868493975])</answer>", "action": "('move', [-3.1001514721830064, -2.271073789623722, -19.308306868493975])", "reward": 0.0, "image": 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"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', [-3.1001514721830064, -2.271073789623722, -19.308306868493975])</answer>", "action": "('move', [-3.1001514721830064, -2.271073789623722, -19.308306868493975])", "reward": 0.0, "image": 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"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": "0151c41e0f7367182e739893308e027804bcc84f66e1a35b8ede323c410d9370"}
+{"episode": 65435, "episode_seed": 1291296515, "split": "train", "timestamp": "2025-09-15T23:57:07.921454", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1291296515, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [4.78402661565704, 11.624657054029377, 42.46145233625246])</answer>", "action": "('move', [4.78402661565704, 11.624657054029377, 42.46145233625246])", "reward": 0.0, "image": 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"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', [4.78402661565704, 11.624657054029377, 42.46145233625246])</answer>", "action": "('move', [4.78402661565704, 11.624657054029377, 42.46145233625246])", "reward": 0.0, "image": 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"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', [4.78402661565704, 11.624657054029377, 42.46145233625246])</answer>", "action": "('move', [4.78402661565704, 11.624657054029377, 42.46145233625246])", "reward": 0.0, "image": 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"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": "45e00d8ea94075d0b3bbc7babdabe318a917a7afa83c926fadca1da65100997e"}
+{"episode": 65436, "episode_seed": 2111099322, "split": "train", "timestamp": "2025-09-15T23:57:08.524267", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2111099322, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-3.21013097674443, 0.7530854458618571, 54.71891811172361])</answer>", "action": "('move', [-3.21013097674443, 0.7530854458618571, 54.71891811172361])", "reward": 0.0, "image": 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"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', [-3.21013097674443, 0.7530854458618571, 54.71891811172361])</answer>", "action": "('move', [-3.21013097674443, 0.7530854458618571, 54.71891811172361])", "reward": 0.0, "image": 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"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', [-3.21013097674443, 0.7530854458618571, 54.71891811172361])</answer>", "action": "('move', [-3.21013097674443, 0.7530854458618571, 54.71891811172361])", "reward": 0.0, "image": 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"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": "8570d6fe8efa0abe93e40d9fcfd03453950fb687ae0ea419d594e3cbed69b326"}
+{"episode": 65437, "episode_seed": 2299865654, "split": "train", "timestamp": "2025-09-15T23:57:09.462002", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2299865654, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-2.262879205925907, -0.030609513293038446, 39.98336958675645])</answer>", "action": "('move', [-2.262879205925907, -0.030609513293038446, 39.98336958675645])", "reward": 0.0, "image": 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"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', [-2.262879205925907, -0.030609513293038446, 39.98336958675645])</answer>", "action": "('move', [-2.262879205925907, -0.030609513293038446, 39.98336958675645])", "reward": 0.0, "image": 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"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', [-2.262879205925907, -0.030609513293038446, 39.98336958675645])</answer>", "action": "('move', [-2.262879205925907, -0.030609513293038446, 39.98336958675645])", "reward": 0.0, "image": 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"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": "2b0978e0551353f43f3f57b637084b8de57443ff74879e33bc2646cd7fccd714"}
+{"episode": 65438, "episode_seed": 3257976653, "split": "train", "timestamp": "2025-09-15T23:57:10.068585", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3257976653, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [3.751254556238564, -0.7959783978935202, 23.033403304682157])</answer>", "action": "('move', [3.751254556238564, -0.7959783978935202, 23.033403304682157])", "reward": 0.0, "image": 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"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', [3.751254556238564, -0.7959783978935202, 23.033403304682157])</answer>", "action": "('move', [3.751254556238564, -0.7959783978935202, 23.033403304682157])", "reward": 0.0, "image": 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"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', [3.751254556238564, -0.7959783978935202, 23.033403304682157])</answer>", "action": "('move', [3.751254556238564, -0.7959783978935202, 23.033403304682157])", "reward": 0.0, "image": 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"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": "3174d35ff297f994192ce42a66fb4e576de0077ee21e164ec051c9ed485d0e93"}
+{"episode": 65439, "episode_seed": 3194145950, "split": "train", "timestamp": "2025-09-15T23:57:10.674331", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3194145950, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [2.054071652948731, -6.5536076601632764, -29.31641410320243])</answer>", "action": "('move', [2.054071652948731, -6.5536076601632764, -29.31641410320243])", "reward": 0.0, "image": 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"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', [2.054071652948731, -6.5536076601632764, -29.31641410320243])</answer>", "action": "('move', [2.054071652948731, -6.5536076601632764, -29.31641410320243])", "reward": 0.0, "image": 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"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', [2.054071652948731, -6.5536076601632764, -29.31641410320243])</answer>", "action": "('move', [2.054071652948731, -6.5536076601632764, -29.31641410320243])", "reward": 0.0, "image": 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"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": "06f57c75600cd4c2687ed1f493c435caae84e592f07cee9b90ba81d3b76fe6ff"}
+{"episode": 65440, "episode_seed": 3956426134, "split": "train", "timestamp": "2025-09-15T23:57:11.287756", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3956426134, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [91.68065839001382, -29.539491979534787, -53.7119175558067])</answer>", "action": "('move', [91.68065839001382, -29.539491979534787, -53.7119175558067])", "reward": 0.0, "image": 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"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', [91.68065839001382, -29.539491979534787, -53.7119175558067])</answer>", "action": "('move', [91.68065839001382, -29.539491979534787, -53.7119175558067])", "reward": 0.0, "image": 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"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', [91.68065839001382, -29.539491979534787, -53.7119175558067])</answer>", "action": "('move', [91.68065839001382, -29.539491979534787, -53.7119175558067])", "reward": 0.0, "image": 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"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": "c2de5ebf025193d717bbd74b0c1ed14bb144ab0436a134c14b867605e70d43ac"}
+{"episode": 65441, "episode_seed": 676197029, "split": "train", "timestamp": "2025-09-15T23:57:12.224847", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 676197029, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-0.1179924674981775, 2.398700776566201, 26.249212743711457])</answer>", "action": "('move', [-0.1179924674981775, 2.398700776566201, 26.249212743711457])", "reward": 0.0, "image": 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"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', [-0.1179924674981775, 2.398700776566201, 26.249212743711457])</answer>", "action": "('move', [-0.1179924674981775, 2.398700776566201, 26.249212743711457])", "reward": 0.0, "image": 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"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', [-0.1179924674981775, 2.398700776566201, 26.249212743711457])</answer>", "action": "('move', [-0.1179924674981775, 2.398700776566201, 26.249212743711457])", "reward": 0.0, "image": 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"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": "4e56c0993cab07c12a3477cd5985c5aa5a946539b835560c947c0795a65549fa"}
+{"episode": 65442, "episode_seed": 4091977682, "split": "train", "timestamp": "2025-09-15T23:57:12.833836", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 4091977682, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [4.989714577491113, -9.933292367943075, 13.377754494353553])</answer>", "action": "('move', [4.989714577491113, -9.933292367943075, 13.377754494353553])", "reward": 0.0, "image": 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"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', [4.989714577491113, -9.933292367943075, 13.377754494353553])</answer>", "action": "('move', [4.989714577491113, -9.933292367943075, 13.377754494353553])", "reward": 0.0, "image": 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"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', [4.989714577491113, -9.933292367943075, 13.377754494353553])</answer>", "action": "('move', [4.989714577491113, -9.933292367943075, 13.377754494353553])", "reward": 0.0, "image": 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"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": "62383338720e7270ee61c1ef87040c46d9392acfbb63f24e3e6b189ed3692383"}
+{"episode": 65443, "episode_seed": 1286049177, "split": "train", "timestamp": "2025-09-15T23:57:13.438547", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1286049177, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [4.499787857161546, -4.156572064869459, -29.442458023409653])</answer>", "action": "('move', [4.499787857161546, -4.156572064869459, -29.442458023409653])", "reward": 0.0, "image": 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"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', [4.499787857161546, -4.156572064869459, -29.442458023409653])</answer>", "action": "('move', [4.499787857161546, -4.156572064869459, -29.442458023409653])", "reward": 0.0, "image": 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"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', [4.499787857161546, -4.156572064869459, -29.442458023409653])</answer>", "action": "('move', [4.499787857161546, -4.156572064869459, -29.442458023409653])", "reward": 0.0, "image": 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"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": "9327932087d1d90598265b0eec697ed17f0b588e303ae2cf0d5382436266664d"}
+{"episode": 65444, "episode_seed": 1093795806, "split": "train", "timestamp": "2025-09-15T23:57:14.378160", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1093795806, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-1.088731135509606, 0.21756843021603703, 32.63804952917341])</answer>", "action": "('move', [-1.088731135509606, 0.21756843021603703, 32.63804952917341])", "reward": 0.0, "image": 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"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', [-1.088731135509606, 0.21756843021603703, 32.63804952917341])</answer>", "action": "('move', [-1.088731135509606, 0.21756843021603703, 32.63804952917341])", "reward": 0.0, "image": 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"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', [-1.088731135509606, 0.21756843021603703, 32.63804952917341])</answer>", "action": "('move', [-1.088731135509606, 0.21756843021603703, 32.63804952917341])", "reward": 0.0, "image": 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"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": "4310b807078645b434cb90f68f3734b4b559ec614d6f113083e50264ab03971e"}
+{"episode": 65445, "episode_seed": 3797439006, "split": "train", "timestamp": "2025-09-15T23:57:14.985712", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3797439006, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [0.9582063915729667, 7.521123243218671, -20.39280823582529])</answer>", "action": "('move', [0.9582063915729667, 7.521123243218671, -20.39280823582529])", "reward": 0.0, "image": 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"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', [0.9582063915729667, 7.521123243218671, -20.39280823582529])</answer>", "action": "('move', [0.9582063915729667, 7.521123243218671, -20.39280823582529])", "reward": 0.0, "image": 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"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', [0.9582063915729667, 7.521123243218671, -20.39280823582529])</answer>", "action": "('move', [0.9582063915729667, 7.521123243218671, -20.39280823582529])", "reward": 0.0, "image": 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"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": "8db5c13199c5c6d043290176ff5158c06b9cc2c79215e560f9034d4444d3d2a8"}
+{"episode": 65446, "episode_seed": 1007183104, "split": "train", "timestamp": "2025-09-15T23:57:15.594365", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1007183104, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-6.573541616838097, -11.955207800735842, 28.588813883252573])</answer>", "action": "('move', [-6.573541616838097, -11.955207800735842, 28.588813883252573])", "reward": 0.0, "image": 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"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', [-6.573541616838097, -11.955207800735842, 28.588813883252573])</answer>", "action": "('move', [-6.573541616838097, -11.955207800735842, 28.588813883252573])", "reward": 0.0, "image": 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"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', [-6.573541616838097, -11.955207800735842, 28.588813883252573])</answer>", "action": "('move', [-6.573541616838097, -11.955207800735842, 28.588813883252573])", "reward": 0.0, "image": 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"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": "899b1be8212cc5c1ec30a55c375f763cb261a2c267f8e28a51e147023df21984"}
+{"episode": 65447, "episode_seed": 535196892, "split": "train", "timestamp": "2025-09-15T23:57:16.206247", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 535196892, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-38.89501964050233, 42.92552754998366, 12.71726979685144])</answer>", "action": "('move', [-38.89501964050233, 42.92552754998366, 12.71726979685144])", "reward": 0.0, "image": 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"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', [-38.89501964050233, 42.92552754998366, 12.71726979685144])</answer>", "action": "('move', [-38.89501964050233, 42.92552754998366, 12.71726979685144])", "reward": 0.0, "image": 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"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', [-38.89501964050233, 42.92552754998366, 12.71726979685144])</answer>", "action": "('move', [-38.89501964050233, 42.92552754998366, 12.71726979685144])", "reward": 0.0, "image": 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"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": "d8c91a4b9da0d4ff8f4e1108982e9b561ba579686a47705bbc40279bf2f88d22"}
+{"episode": 65448, "episode_seed": 3195056736, "split": "train", "timestamp": "2025-09-15T23:57:17.145347", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3195056736, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [0.8560475054327031, 0.375229549110408, -20.580566261608727])</answer>", "action": "('move', [0.8560475054327031, 0.375229549110408, -20.580566261608727])", "reward": 0.0, "image": 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"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', [0.8560475054327031, 0.375229549110408, -20.580566261608727])</answer>", "action": "('move', [0.8560475054327031, 0.375229549110408, -20.580566261608727])", "reward": 0.0, "image": 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"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', [0.8560475054327031, 0.375229549110408, -20.580566261608727])</answer>", "action": "('move', [0.8560475054327031, 0.375229549110408, -20.580566261608727])", "reward": 0.0, "image": 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"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": "b27be62a0663513f7e1f7f14c3b001a8184d04d6555215f673aae497a5080954"}
+{"episode": 65449, "episode_seed": 2110514172, "split": "train", "timestamp": "2025-09-15T23:57:17.751679", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2110514172, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-48.844919322933954, 6.1566651267218795, 37.25220841470741])</answer>", "action": "('move', [-48.844919322933954, 6.1566651267218795, 37.25220841470741])", "reward": 0.0, "image": 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"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', [-48.844919322933954, 6.1566651267218795, 37.25220841470741])</answer>", "action": "('move', [-48.844919322933954, 6.1566651267218795, 37.25220841470741])", "reward": 0.0, "image": 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"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', [-48.844919322933954, 6.1566651267218795, 37.25220841470741])</answer>", "action": "('move', [-48.844919322933954, 6.1566651267218795, 37.25220841470741])", "reward": 0.0, "image": 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"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": "5090ab94ec7b7f28af2587993c5c5b3a83dc8476387cd49301ec8b5ff2bc023b"}
+{"episode": 65450, "episode_seed": 475077977, "split": "train", "timestamp": "2025-09-15T23:57:18.355010", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 475077977, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [3.759271171097728, 0.6590930833530984, -15.10104167697265])</answer>", "action": "('move', [3.759271171097728, 0.6590930833530984, -15.10104167697265])", "reward": 0.0, "image": 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"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', [3.759271171097728, 0.6590930833530984, -15.10104167697265])</answer>", "action": "('move', [3.759271171097728, 0.6590930833530984, -15.10104167697265])", "reward": 0.0, "image": 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"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', [3.759271171097728, 0.6590930833530984, -15.10104167697265])</answer>", "action": "('move', [3.759271171097728, 0.6590930833530984, -15.10104167697265])", "reward": 0.0, "image": 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"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": "f3e5a8cbb37dc8fec13db2656b1b02a8b301130601e9daa4dc9dd414bcf8730f"}
+{"episode": 65451, "episode_seed": 3418206950, "split": "train", "timestamp": "2025-09-15T23:57:19.295866", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3418206950, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-4.520176786413251, -6.2937405272656415, -12.910476256582115])</answer>", "action": "('move', [-4.520176786413251, -6.2937405272656415, -12.910476256582115])", "reward": 0.0, "image": 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"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', [-4.520176786413251, -6.2937405272656415, -12.910476256582115])</answer>", "action": "('move', [-4.520176786413251, -6.2937405272656415, -12.910476256582115])", "reward": 0.0, "image": 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"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', [-4.520176786413251, -6.2937405272656415, -12.910476256582115])</answer>", "action": "('move', [-4.520176786413251, -6.2937405272656415, -12.910476256582115])", "reward": 0.0, "image": 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"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": "50a897421cdc0bdb0d0ba2bb31965abd8534f7eca2f651ba064aa0c2011ddad8"}
+{"episode": 65452, "episode_seed": 10810569, "split": "train", "timestamp": "2025-09-15T23:57:19.906344", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 10810569, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [21.245181883540244, -6.792694611620008, -40.95644138533461])</answer>", "action": "('move', [21.245181883540244, -6.792694611620008, -40.95644138533461])", "reward": 0.0, "image": 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"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', [21.245181883540244, -6.792694611620008, -40.95644138533461])</answer>", "action": "('move', [21.245181883540244, -6.792694611620008, -40.95644138533461])", "reward": 0.0, "image": 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"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', [21.245181883540244, -6.792694611620008, -40.95644138533461])</answer>", "action": "('move', [21.245181883540244, -6.792694611620008, -40.95644138533461])", "reward": 0.0, "image": 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"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": "e6f169090d58e3eee4bc81f6076f892dbc08f14b7e1b735d230711b2d3a1b0c9"}
+{"episode": 65453, "episode_seed": 1685232265, "split": "train", "timestamp": "2025-09-15T23:57:20.510381", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1685232265, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [2.6176206269165028, 10.386496526957545, -45.75083035772485])</answer>", "action": "('move', [2.6176206269165028, 10.386496526957545, -45.75083035772485])", "reward": 0.0, "image": 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"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', [2.6176206269165028, 10.386496526957545, -45.75083035772485])</answer>", "action": "('move', [2.6176206269165028, 10.386496526957545, -45.75083035772485])", "reward": 0.0, "image": 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"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', [2.6176206269165028, 10.386496526957545, -45.75083035772485])</answer>", "action": "('move', [2.6176206269165028, 10.386496526957545, -45.75083035772485])", "reward": 0.0, "image": 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"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": "f9180d5ba3990b99faea0e119e060e5442c4465ed38a8ead514f17468c4f63a4"}
+{"episode": 65454, "episode_seed": 3740462556, "split": "train", "timestamp": "2025-09-15T23:57:21.119674", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3740462556, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-2.8207788736088517, 0.4109706404836336, 34.113131780903245])</answer>", "action": "('move', [-2.8207788736088517, 0.4109706404836336, 34.113131780903245])", "reward": 0.0, "image": 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"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', [-2.8207788736088517, 0.4109706404836336, 34.113131780903245])</answer>", "action": "('move', [-2.8207788736088517, 0.4109706404836336, 34.113131780903245])", "reward": 0.0, "image": 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"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', [-2.8207788736088517, 0.4109706404836336, 34.113131780903245])</answer>", "action": "('move', [-2.8207788736088517, 0.4109706404836336, 34.113131780903245])", "reward": 0.0, "image": 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"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": "e2f8c2ebdf863305c064a0447016377dd759a89c6dec4e07ceefe9d97b0e3d23"}
+{"episode": 65455, "episode_seed": 2500124296, "split": "train", "timestamp": "2025-09-15T23:57:22.066337", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2500124296, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [0.3124637830950614, 8.204257533029693, 19.69408521833579])</answer>", "action": "('move', [0.3124637830950614, 8.204257533029693, 19.69408521833579])", "reward": 0.0, "image": 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"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', [0.3124637830950614, 8.204257533029693, 19.69408521833579])</answer>", "action": "('move', [0.3124637830950614, 8.204257533029693, 19.69408521833579])", "reward": 0.0, "image": 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"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', [0.3124637830950614, 8.204257533029693, 19.69408521833579])</answer>", "action": "('move', [0.3124637830950614, 8.204257533029693, 19.69408521833579])", "reward": 0.0, "image": 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"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": "be13361e8de9d4f4a0e05a4711b0a26cf9e70403493563eb0a8ff9c8add3c672"}
+{"episode": 65456, "episode_seed": 2808902442, "split": "train", "timestamp": "2025-09-15T23:57:22.679081", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2808902442, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [6.301315311388503, 14.708743926444022, -2.827385968945862])</answer>", "action": "('move', [6.301315311388503, 14.708743926444022, -2.827385968945862])", "reward": 0.0, "image": 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"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', [6.301315311388503, 14.708743926444022, -2.827385968945862])</answer>", "action": "('move', [6.301315311388503, 14.708743926444022, -2.827385968945862])", "reward": 0.0, "image": 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"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', [6.301315311388503, 14.708743926444022, -2.827385968945862])</answer>", "action": "('move', [6.301315311388503, 14.708743926444022, -2.827385968945862])", "reward": 0.0, "image": 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"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": "531cfb1041f5c290d1e5b4c388f06481306f59c4bd77edf01fc54421efe834ed"}
+{"episode": 65457, "episode_seed": 1207875134, "split": "train", "timestamp": "2025-09-15T23:57:23.285956", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1207875134, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-1.1751499808686843, 4.904149246293702, 57.1877142725639])</answer>", "action": "('move', [-1.1751499808686843, 4.904149246293702, 57.1877142725639])", "reward": 0.0, "image": 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"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', [-1.1751499808686843, 4.904149246293702, 57.1877142725639])</answer>", "action": "('move', [-1.1751499808686843, 4.904149246293702, 57.1877142725639])", "reward": 0.0, "image": 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"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', [-1.1751499808686843, 4.904149246293702, 57.1877142725639])</answer>", "action": "('move', [-1.1751499808686843, 4.904149246293702, 57.1877142725639])", "reward": 0.0, "image": 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"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": "6bb200c664bd58570592c36d77084c393e2b0a2e1168d598f682090d9c16c725"}
+{"episode": 65458, "episode_seed": 2013759869, "split": "train", "timestamp": "2025-09-15T23:57:24.241547", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2013759869, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [2.4802718796157404, -3.192978316491587, -27.337374650037628])</answer>", "action": "('move', [2.4802718796157404, -3.192978316491587, -27.337374650037628])", "reward": 0.0, "image": 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"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', [2.4802718796157404, -3.192978316491587, -27.337374650037628])</answer>", "action": "('move', [2.4802718796157404, -3.192978316491587, -27.337374650037628])", "reward": 0.0, "image": 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"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', [2.4802718796157404, -3.192978316491587, -27.337374650037628])</answer>", "action": "('move', [2.4802718796157404, -3.192978316491587, -27.337374650037628])", "reward": 0.0, "image": 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"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": "f303c4fa2a087583ff150e213dfa87c7899b2857020300699d340d6e5497a270"}
+{"episode": 65459, "episode_seed": 2762315420, "split": "train", "timestamp": "2025-09-15T23:57:24.853038", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2762315420, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [26.95142828244271, 106.40891384097618, -14.612442249282026])</answer>", "action": "('move', [26.95142828244271, 106.40891384097618, -14.612442249282026])", "reward": 0.0, "image": 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"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', [26.95142828244271, 106.40891384097618, -14.612442249282026])</answer>", "action": "('move', [26.95142828244271, 106.40891384097618, -14.612442249282026])", "reward": 0.0, "image": 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"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', [26.95142828244271, 106.40891384097618, -14.612442249282026])</answer>", "action": "('move', [26.95142828244271, 106.40891384097618, -14.612442249282026])", "reward": 0.0, "image": 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"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": "baf55f7a7ebaf2cdd397208c319a29f4f5a604fb7064f314555c9c7932cbca44"}
+{"episode": 65460, "episode_seed": 3680415410, "split": "train", "timestamp": "2025-09-15T23:57:25.462281", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3680415410, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-90.1987977363353, 30.717958556762607, -26.996126526284286])</answer>", "action": "('move', [-90.1987977363353, 30.717958556762607, -26.996126526284286])", "reward": 0.0, "image": 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"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', [-90.1987977363353, 30.717958556762607, -26.996126526284286])</answer>", "action": "('move', [-90.1987977363353, 30.717958556762607, -26.996126526284286])", "reward": 0.0, "image": 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"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', [-90.1987977363353, 30.717958556762607, -26.996126526284286])</answer>", "action": "('move', [-90.1987977363353, 30.717958556762607, -26.996126526284286])", "reward": 0.0, "image": 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"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": "a3d4f73bbde5f2abdd7f4ab84ad9b7ac8f6b0134910b62cae3d69746917f2fb2"}
+{"episode": 65461, "episode_seed": 3474827706, "split": "train", "timestamp": "2025-09-15T23:57:26.067431", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3474827706, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-3.813769144244984, -0.03727861916380509, 44.58444123141529])</answer>", "action": "('move', [-3.813769144244984, -0.03727861916380509, 44.58444123141529])", "reward": 0.0, "image": 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"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', [-3.813769144244984, -0.03727861916380509, 44.58444123141529])</answer>", "action": "('move', [-3.813769144244984, -0.03727861916380509, 44.58444123141529])", "reward": 0.0, "image": 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"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', [-3.813769144244984, -0.03727861916380509, 44.58444123141529])</answer>", "action": "('move', [-3.813769144244984, -0.03727861916380509, 44.58444123141529])", "reward": 0.0, "image": 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"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": "8bda613d1336dfb4bc1f2b8f842c289315f428fd2d5828085af5c9f479607e6b"}
+{"episode": 65462, "episode_seed": 2851784170, "split": "train", "timestamp": "2025-09-15T23:57:27.010655", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2851784170, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-31.30118834005898, -27.74115904492055, 53.49054935251498])</answer>", "action": "('move', [-31.30118834005898, -27.74115904492055, 53.49054935251498])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-31.30118834005898, -27.74115904492055, 53.49054935251498])</answer>", "action": "('move', [-31.30118834005898, -27.74115904492055, 53.49054935251498])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-31.30118834005898, -27.74115904492055, 53.49054935251498])</answer>", "action": "('move', [-31.30118834005898, -27.74115904492055, 53.49054935251498])", "reward": 0.0, "image": 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"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": "3eb1dcac4ef75981ad8317d7c89c078b86c28e3f1fe9f6076ac37a8c7299c17a"}
+{"episode": 65463, "episode_seed": 1527584951, "split": "train", "timestamp": "2025-09-15T23:57:27.620043", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1527584951, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [2.0315362607183896, 7.744309441950219, 13.956952506864601])</answer>", "action": "('move', [2.0315362607183896, 7.744309441950219, 13.956952506864601])", "reward": 0.0, "image": 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"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', [2.0315362607183896, 7.744309441950219, 13.956952506864601])</answer>", "action": "('move', [2.0315362607183896, 7.744309441950219, 13.956952506864601])", "reward": 0.0, "image": 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"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', [2.0315362607183896, 7.744309441950219, 13.956952506864601])</answer>", "action": "('move', [2.0315362607183896, 7.744309441950219, 13.956952506864601])", "reward": 0.0, "image": 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"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": "423ea1899790213d81ece454283825e55e4b82397bfe40c3044a2a4821516b42"}
+{"episode": 65464, "episode_seed": 3870822372, "split": "train", "timestamp": "2025-09-15T23:57:28.223729", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3870822372, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-4.441370244479796, 2.2265782587914686, 51.85983965773281])</answer>", "action": "('move', [-4.441370244479796, 2.2265782587914686, 51.85983965773281])", "reward": 0.0, "image": 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"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', [-4.441370244479796, 2.2265782587914686, 51.85983965773281])</answer>", "action": "('move', [-4.441370244479796, 2.2265782587914686, 51.85983965773281])", "reward": 0.0, "image": 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"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', [-4.441370244479796, 2.2265782587914686, 51.85983965773281])</answer>", "action": "('move', [-4.441370244479796, 2.2265782587914686, 51.85983965773281])", "reward": 0.0, "image": 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"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": "7de275bda306e68d61229d87462687b2f165747dee78a69ca54cf65993880834"}
+{"episode": 65465, "episode_seed": 2276105179, "split": "train", "timestamp": "2025-09-15T23:57:29.155620", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2276105179, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-2.5532483173883245, -0.38776396824435894, -34.24359257442323])</answer>", "action": "('move', [-2.5532483173883245, -0.38776396824435894, -34.24359257442323])", "reward": 0.0, "image": 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"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', [-2.5532483173883245, -0.38776396824435894, -34.24359257442323])</answer>", "action": "('move', [-2.5532483173883245, -0.38776396824435894, -34.24359257442323])", "reward": 0.0, "image": 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"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', [-2.5532483173883245, -0.38776396824435894, -34.24359257442323])</answer>", "action": "('move', [-2.5532483173883245, -0.38776396824435894, -34.24359257442323])", "reward": 0.0, "image": 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"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": "44e9b6337bff55587b96d824340778436ff1e8c596a6a4fe5ce6bfda2748d014"}
+{"episode": 65466, "episode_seed": 15513700, "split": "train", "timestamp": "2025-09-15T23:57:29.764426", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 15513700, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [7.207921660731473, 14.281627699158145, 27.97158537760286])</answer>", "action": "('move', [7.207921660731473, 14.281627699158145, 27.97158537760286])", "reward": 0.0, "image": 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"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', [7.207921660731473, 14.281627699158145, 27.97158537760286])</answer>", "action": "('move', [7.207921660731473, 14.281627699158145, 27.97158537760286])", "reward": 0.0, "image": 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"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', [7.207921660731473, 14.281627699158145, 27.97158537760286])</answer>", "action": "('move', [7.207921660731473, 14.281627699158145, 27.97158537760286])", "reward": 0.0, "image": 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"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": "c768c2b1d2b4c98214ee9585ac59aa906176c54f40ff043859e2bbce39da9815"}
+{"episode": 65467, "episode_seed": 4185241789, "split": "train", "timestamp": "2025-09-15T23:57:30.369379", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 4185241789, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-17.75120928015772, -14.113246341552994, 16.886978950099813])</answer>", "action": "('move', [-17.75120928015772, -14.113246341552994, 16.886978950099813])", "reward": 0.0, "image": 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"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', [-17.75120928015772, -14.113246341552994, 16.886978950099813])</answer>", "action": "('move', [-17.75120928015772, -14.113246341552994, 16.886978950099813])", "reward": 0.0, "image": 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"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', [-17.75120928015772, -14.113246341552994, 16.886978950099813])</answer>", "action": "('move', [-17.75120928015772, -14.113246341552994, 16.886978950099813])", "reward": 0.0, "image": 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"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": "17790c69c87545801a7cfb68d79fe9e453e7605f91e24a2bef0291e597d4529a"}
+{"episode": 65468, "episode_seed": 2417099165, "split": "train", "timestamp": "2025-09-15T23:57:30.976451", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2417099165, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [14.994371152477507, 7.158757856446968, 13.274904182675963])</answer>", "action": "('move', [14.994371152477507, 7.158757856446968, 13.274904182675963])", "reward": 0.0, "image": 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"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', [14.994371152477507, 7.158757856446968, 13.274904182675963])</answer>", "action": "('move', [14.994371152477507, 7.158757856446968, 13.274904182675963])", "reward": 0.0, "image": 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"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', [14.994371152477507, 7.158757856446968, 13.274904182675963])</answer>", "action": "('move', [14.994371152477507, 7.158757856446968, 13.274904182675963])", "reward": 0.0, "image": 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"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": "97398d5ec8bb989cfd3810b84d56f0cec96b8a763f804ac1614b7a79ed68f7ed"}
+{"episode": 65469, "episode_seed": 536340271, "split": "train", "timestamp": "2025-09-15T23:57:31.913487", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 536340271, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-0.96211057154225, -3.3350697479391354, 17.694216661792172])</answer>", "action": "('move', [-0.96211057154225, -3.3350697479391354, 17.694216661792172])", "reward": 0.0, "image": 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"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', [-0.96211057154225, -3.3350697479391354, 17.694216661792172])</answer>", "action": "('move', [-0.96211057154225, -3.3350697479391354, 17.694216661792172])", "reward": 0.0, "image": 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"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', [-0.96211057154225, -3.3350697479391354, 17.694216661792172])</answer>", "action": "('move', [-0.96211057154225, -3.3350697479391354, 17.694216661792172])", "reward": 0.0, "image": 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"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": "d667d921cd66b95446efeae32d580c95633d857ed160a0a56b3c88d86ba503aa"}
+{"episode": 65470, "episode_seed": 207837667, "split": "train", "timestamp": "2025-09-15T23:57:32.526889", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 207837667, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-5.522111921647117, 6.961944309512731, -39.287543510674276])</answer>", "action": "('move', [-5.522111921647117, 6.961944309512731, -39.287543510674276])", "reward": 0.0, "image": 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"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', [-5.522111921647117, 6.961944309512731, -39.287543510674276])</answer>", "action": "('move', [-5.522111921647117, 6.961944309512731, -39.287543510674276])", "reward": 0.0, "image": 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"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', [-5.522111921647117, 6.961944309512731, -39.287543510674276])</answer>", "action": "('move', [-5.522111921647117, 6.961944309512731, -39.287543510674276])", "reward": 0.0, "image": 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"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": "15db173b38ad98dfe7d1f7c753f32c4c463998e6e06991929b7b982c3e2d5cf6"}
+{"episode": 65471, "episode_seed": 691679521, "split": "train", "timestamp": "2025-09-15T23:57:33.129853", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 691679521, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [1.6544185018978135, -0.2273124652910489, 5.940929006303672])</answer>", "action": "('move', [1.6544185018978135, -0.2273124652910489, 5.940929006303672])", "reward": 0.0, "image": 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"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', [1.6544185018978135, -0.2273124652910489, 5.940929006303672])</answer>", "action": "('move', [1.6544185018978135, -0.2273124652910489, 5.940929006303672])", "reward": 0.0, "image": 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"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', [1.6544185018978135, -0.2273124652910489, 5.940929006303672])</answer>", "action": "('move', [1.6544185018978135, -0.2273124652910489, 5.940929006303672])", "reward": 0.0, "image": 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"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": "b8bb8acb0d95b8e284a3b5052f3e63b9136e6654e5d69d5d63542fd4e15fd781"}
+{"episode": 65472, "episode_seed": 1056654077, "split": "train", "timestamp": "2025-09-15T23:57:33.731148", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1056654077, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [208.85545701115112, 197.00117855785984, -44.70801322412412])</answer>", "action": "('move', [208.85545701115112, 197.00117855785984, -44.70801322412412])", "reward": 0.0, "image": 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"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', [208.85545701115112, 197.00117855785984, -44.70801322412412])</answer>", "action": "('move', [208.85545701115112, 197.00117855785984, -44.70801322412412])", "reward": 0.0, "image": 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"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', [208.85545701115112, 197.00117855785984, -44.70801322412412])</answer>", "action": "('move', [208.85545701115112, 197.00117855785984, -44.70801322412412])", "reward": 0.0, "image": 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"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": "d79249929443358e200c106428beeb2268a22dcd4fdf4a6b67854e67d63e680d"}
+{"episode": 65473, "episode_seed": 565418950, "split": "train", "timestamp": "2025-09-15T23:57:34.673163", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 565418950, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [5.890456850828293, 4.870985893874063, -32.55618764290302])</answer>", "action": "('move', [5.890456850828293, 4.870985893874063, -32.55618764290302])", "reward": 0.0, "image": 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"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', [5.890456850828293, 4.870985893874063, -32.55618764290302])</answer>", "action": "('move', [5.890456850828293, 4.870985893874063, -32.55618764290302])", "reward": 0.0, "image": 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"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', [5.890456850828293, 4.870985893874063, -32.55618764290302])</answer>", "action": "('move', [5.890456850828293, 4.870985893874063, -32.55618764290302])", "reward": 0.0, "image": 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"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": "6a3fed0af4ab67040c6097af4918d8b864c0773822411e426bb1ade7fecacddf"}
+{"episode": 65474, "episode_seed": 4094561778, "split": "train", "timestamp": "2025-09-15T23:57:35.282104", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 4094561778, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [4.0759572389063345, -22.155917002906975, -19.539838028111927])</answer>", "action": "('move', [4.0759572389063345, -22.155917002906975, -19.539838028111927])", "reward": 0.0, "image": 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"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', [4.0759572389063345, -22.155917002906975, -19.539838028111927])</answer>", "action": "('move', [4.0759572389063345, -22.155917002906975, -19.539838028111927])", "reward": 0.0, "image": 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"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', [4.0759572389063345, -22.155917002906975, -19.539838028111927])</answer>", "action": "('move', [4.0759572389063345, -22.155917002906975, -19.539838028111927])", "reward": 0.0, "image": 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"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": "df8c13ebec2d95118055ccdcd3df966947a3d6235495a6b81b8e308346cf0d96"}
+{"episode": 65475, "episode_seed": 1454510236, "split": "train", "timestamp": "2025-09-15T23:57:35.885998", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1454510236, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-3.343907224297574, -5.969499949485852, 24.745039939984242])</answer>", "action": "('move', [-3.343907224297574, -5.969499949485852, 24.745039939984242])", "reward": 0.0, "image": 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"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', [-3.343907224297574, -5.969499949485852, 24.745039939984242])</answer>", "action": "('move', [-3.343907224297574, -5.969499949485852, 24.745039939984242])", "reward": 0.0, "image": 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"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', [-3.343907224297574, -5.969499949485852, 24.745039939984242])</answer>", "action": "('move', [-3.343907224297574, -5.969499949485852, 24.745039939984242])", "reward": 0.0, "image": 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"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": "f7cbf9326ae24f1992b941a772a37b2cf50f8134ad5cda9df7920746161739bb"}
+{"episode": 65476, "episode_seed": 2912489905, "split": "train", "timestamp": "2025-09-15T23:57:36.832971", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2912489905, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-3.5533134978303718, -2.4926105923979596, 43.692525986913985])</answer>", "action": "('move', [-3.5533134978303718, -2.4926105923979596, 43.692525986913985])", "reward": 0.0, "image": 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"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', [-3.5533134978303718, -2.4926105923979596, 43.692525986913985])</answer>", "action": "('move', [-3.5533134978303718, -2.4926105923979596, 43.692525986913985])", "reward": 0.0, "image": 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"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', [-3.5533134978303718, -2.4926105923979596, 43.692525986913985])</answer>", "action": "('move', [-3.5533134978303718, -2.4926105923979596, 43.692525986913985])", "reward": 0.0, "image": 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"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": "4154e1581659d7f8cae802ab49d10c985cb6a465bca0fe6091de11cb7a9cd4a2"}
+{"episode": 65477, "episode_seed": 4125192719, "split": "train", "timestamp": "2025-09-15T23:57:37.444583", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 4125192719, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [13.780573905098125, -22.061800142040877, 21.538212663739614])</answer>", "action": "('move', [13.780573905098125, -22.061800142040877, 21.538212663739614])", "reward": 0.0, "image": 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"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', [13.780573905098125, -22.061800142040877, 21.538212663739614])</answer>", "action": "('move', [13.780573905098125, -22.061800142040877, 21.538212663739614])", "reward": 0.0, "image": 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"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', [13.780573905098125, -22.061800142040877, 21.538212663739614])</answer>", "action": "('move', [13.780573905098125, -22.061800142040877, 21.538212663739614])", "reward": 0.0, "image": 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"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": "8c302d80e38897c3893a1b51ab1d876c459a825e1ed03be3655d30bcdca703af"}
+{"episode": 65478, "episode_seed": 3146287007, "split": "train", "timestamp": "2025-09-15T23:57:38.052566", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3146287007, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [1.7555940479521939, 1.5144660076272471, -57.101103549964556])</answer>", "action": "('move', [1.7555940479521939, 1.5144660076272471, -57.101103549964556])", "reward": 0.0, "image": 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"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', [1.7555940479521939, 1.5144660076272471, -57.101103549964556])</answer>", "action": "('move', [1.7555940479521939, 1.5144660076272471, -57.101103549964556])", "reward": 0.0, "image": 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"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', [1.7555940479521939, 1.5144660076272471, -57.101103549964556])</answer>", "action": "('move', [1.7555940479521939, 1.5144660076272471, -57.101103549964556])", "reward": 0.0, "image": 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"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": "203b9491618dc0f36369fbe556e311d69d0b53aab282e1c689710d7304794971"}
+{"episode": 65479, "episode_seed": 3516680213, "split": "train", "timestamp": "2025-09-15T23:57:38.662242", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3516680213, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [12.901153547554605, -0.8752217387405699, 54.9896939880231])</answer>", "action": "('move', [12.901153547554605, -0.8752217387405699, 54.9896939880231])", "reward": 0.0, "image": 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"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', [12.901153547554605, -0.8752217387405699, 54.9896939880231])</answer>", "action": "('move', [12.901153547554605, -0.8752217387405699, 54.9896939880231])", "reward": 0.0, "image": 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"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', [12.901153547554605, -0.8752217387405699, 54.9896939880231])</answer>", "action": "('move', [12.901153547554605, -0.8752217387405699, 54.9896939880231])", "reward": 0.0, "image": 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"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": "b4bb0ea5249e8b606dcf4cce52511ecfd11ecfccd9a2ae1fcbc4dbd3574bf118"}
+{"episode": 65480, "episode_seed": 2653615162, "split": "train", "timestamp": "2025-09-15T23:57:39.606936", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2653615162, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-2.205518983174367, 0.18451233007249213, 17.48770070261121])</answer>", "action": "('move', [-2.205518983174367, 0.18451233007249213, 17.48770070261121])", "reward": 0.0, "image": 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"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', [-2.205518983174367, 0.18451233007249213, 17.48770070261121])</answer>", "action": "('move', [-2.205518983174367, 0.18451233007249213, 17.48770070261121])", "reward": 0.0, "image": 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"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', [-2.205518983174367, 0.18451233007249213, 17.48770070261121])</answer>", "action": "('move', [-2.205518983174367, 0.18451233007249213, 17.48770070261121])", "reward": 0.0, "image": 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"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": "96e72c760ade4e03e4f4aae475ff904da36ff1ca96c9584a5a4d851ff26933ba"}
+{"episode": 65481, "episode_seed": 94444459, "split": "train", "timestamp": "2025-09-15T23:57:40.215465", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 94444459, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [4.484411407578963, -1.3240734832024281, 54.16906651229764])</answer>", "action": "('move', [4.484411407578963, -1.3240734832024281, 54.16906651229764])", "reward": 0.0, "image": 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"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', [4.484411407578963, -1.3240734832024281, 54.16906651229764])</answer>", "action": "('move', [4.484411407578963, -1.3240734832024281, 54.16906651229764])", "reward": 0.0, "image": 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"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', [4.484411407578963, -1.3240734832024281, 54.16906651229764])</answer>", "action": "('move', [4.484411407578963, -1.3240734832024281, 54.16906651229764])", "reward": 0.0, "image": 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"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": "e289a1eab4a5ee0c912e667b9d9e398622490afa35b220e0a3e7313634965b9e"}
+{"episode": 65482, "episode_seed": 2308303207, "split": "train", "timestamp": "2025-09-15T23:57:40.820438", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2308303207, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-66.85062795688266, 26.682682276161753, 12.379712752979552])</answer>", "action": "('move', [-66.85062795688266, 26.682682276161753, 12.379712752979552])", "reward": 0.0, "image": 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"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', [-66.85062795688266, 26.682682276161753, 12.379712752979552])</answer>", "action": "('move', [-66.85062795688266, 26.682682276161753, 12.379712752979552])", "reward": 0.0, "image": 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"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', [-66.85062795688266, 26.682682276161753, 12.379712752979552])</answer>", "action": "('move', [-66.85062795688266, 26.682682276161753, 12.379712752979552])", "reward": 0.0, "image": 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"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": "e3bc1dda73650fa39f081e050cb855e9f0817cedced84771e095fc386f111b55"}
+{"episode": 65483, "episode_seed": 4555069, "split": "train", "timestamp": "2025-09-15T23:57:41.765313", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 4555069, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-0.7286087183700048, 3.0886670458343968, 17.505700674699465])</answer>", "action": "('move', [-0.7286087183700048, 3.0886670458343968, 17.505700674699465])", "reward": 0.0, "image": 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"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', [-0.7286087183700048, 3.0886670458343968, 17.505700674699465])</answer>", "action": "('move', [-0.7286087183700048, 3.0886670458343968, 17.505700674699465])", "reward": 0.0, "image": 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"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', [-0.7286087183700048, 3.0886670458343968, 17.505700674699465])</answer>", "action": "('move', [-0.7286087183700048, 3.0886670458343968, 17.505700674699465])", "reward": 0.0, "image": 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"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": "91b389e744f1f35a76d6c260fc81c0daf4e67ac980fdaf96fe78191e0ff35c4e"}
+{"episode": 65484, "episode_seed": 936802238, "split": "train", "timestamp": "2025-09-15T23:57:42.376589", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 936802238, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [0.4701944699037439, -9.571857243055902, 53.160760274892446])</answer>", "action": "('move', [0.4701944699037439, -9.571857243055902, 53.160760274892446])", "reward": 0.0, "image": 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"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', [0.4701944699037439, -9.571857243055902, 53.160760274892446])</answer>", "action": "('move', [0.4701944699037439, -9.571857243055902, 53.160760274892446])", "reward": 0.0, "image": 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"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', [0.4701944699037439, -9.571857243055902, 53.160760274892446])</answer>", "action": "('move', [0.4701944699037439, -9.571857243055902, 53.160760274892446])", "reward": 0.0, "image": 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"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": "9f23e8426bc82cc0ac94dacc7a6780ce014f2588d59bc7363b20761143e11ed0"}
+{"episode": 65485, "episode_seed": 3564657193, "split": "train", "timestamp": "2025-09-15T23:57:42.994502", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3564657193, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [2.170213398799236, -3.3442737404555736, -48.30337543182554])</answer>", "action": "('move', [2.170213398799236, -3.3442737404555736, -48.30337543182554])", "reward": 0.0, "image": 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"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', [2.170213398799236, -3.3442737404555736, -48.30337543182554])</answer>", "action": "('move', [2.170213398799236, -3.3442737404555736, -48.30337543182554])", "reward": 0.0, "image": 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"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', [2.170213398799236, -3.3442737404555736, -48.30337543182554])</answer>", "action": "('move', [2.170213398799236, -3.3442737404555736, -48.30337543182554])", "reward": 0.0, "image": 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"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": "c99b3182a801279810bfda1254a9e2b600035c15103772fbdcbd2060e3cb7209"}
+{"episode": 65486, "episode_seed": 311478667, "split": "train", "timestamp": "2025-09-15T23:57:43.598238", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 311478667, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-18.75280779270619, 17.874687744853862, -16.682324918380953])</answer>", "action": "('move', [-18.75280779270619, 17.874687744853862, -16.682324918380953])", "reward": 0.0, "image": 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"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', [-18.75280779270619, 17.874687744853862, -16.682324918380953])</answer>", "action": "('move', [-18.75280779270619, 17.874687744853862, -16.682324918380953])", "reward": 0.0, "image": 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"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', [-18.75280779270619, 17.874687744853862, -16.682324918380953])</answer>", "action": "('move', [-18.75280779270619, 17.874687744853862, -16.682324918380953])", "reward": 0.0, "image": 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"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": "8381d0b1d742b3f02caaa68b27ded605532759bfeb25f3b99340fedde87296df"}
+{"episode": 65487, "episode_seed": 1540237144, "split": "train", "timestamp": "2025-09-15T23:57:44.587145", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1540237144, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-1.5797632634015486, -2.2807276783701362, -39.891995601120804])</answer>", "action": "('move', [-1.5797632634015486, -2.2807276783701362, -39.891995601120804])", "reward": 0.0, "image": 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"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', [-1.5797632634015486, -2.2807276783701362, -39.891995601120804])</answer>", "action": "('move', [-1.5797632634015486, -2.2807276783701362, -39.891995601120804])", "reward": 0.0, "image": 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"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', [-1.5797632634015486, -2.2807276783701362, -39.891995601120804])</answer>", "action": "('move', [-1.5797632634015486, -2.2807276783701362, -39.891995601120804])", "reward": 0.0, "image": 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"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": "6b6dd90495c87c24e64dcc503a841122d31484ba7dee9766a17eadab735b2bc8"}
+{"episode": 65488, "episode_seed": 1459782149, "split": "train", "timestamp": "2025-09-15T23:57:45.197043", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1459782149, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-2.156797124380963, -1.0902627796959827, -26.348583700498057])</answer>", "action": "('move', [-2.156797124380963, -1.0902627796959827, -26.348583700498057])", "reward": 0.0, "image": 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"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', [-2.156797124380963, -1.0902627796959827, -26.348583700498057])</answer>", "action": "('move', [-2.156797124380963, -1.0902627796959827, -26.348583700498057])", "reward": 0.0, "image": 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"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', [-2.156797124380963, -1.0902627796959827, -26.348583700498057])</answer>", "action": "('move', [-2.156797124380963, -1.0902627796959827, -26.348583700498057])", "reward": 0.0, "image": 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"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": "9c1baf82119ecba6525f49b7335d4373b8f06533c9bc532fdc5196b86f300cb1"}
+{"episode": 65489, "episode_seed": 2307349814, "split": "train", "timestamp": "2025-09-15T23:57:45.798922", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2307349814, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [3.0690549788770816, 3.0590493652875597, -34.81403982196298])</answer>", "action": "('move', [3.0690549788770816, 3.0590493652875597, -34.81403982196298])", "reward": 0.0, "image": 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"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', [3.0690549788770816, 3.0590493652875597, -34.81403982196298])</answer>", "action": "('move', [3.0690549788770816, 3.0590493652875597, -34.81403982196298])", "reward": 0.0, "image": 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"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', [3.0690549788770816, 3.0590493652875597, -34.81403982196298])</answer>", "action": "('move', [3.0690549788770816, 3.0590493652875597, -34.81403982196298])", "reward": 0.0, "image": 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"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": "0f30fd6bfd1a672261ad58660402d6f03c7e38d14df8e08c955dc13757a0dfa2"}
+{"episode": 65490, "episode_seed": 367699548, "split": "train", "timestamp": "2025-09-15T23:57:46.743167", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 367699548, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [3.4494119905766145, -10.156167901825402, -18.167305817539415])</answer>", "action": "('move', [3.4494119905766145, -10.156167901825402, -18.167305817539415])", "reward": 0.0, "image": 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"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', [3.4494119905766145, -10.156167901825402, -18.167305817539415])</answer>", "action": "('move', [3.4494119905766145, -10.156167901825402, -18.167305817539415])", "reward": 0.0, "image": 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"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', [3.4494119905766145, -10.156167901825402, -18.167305817539415])</answer>", "action": "('move', [3.4494119905766145, -10.156167901825402, -18.167305817539415])", "reward": 0.0, "image": 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"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": "91392b48915126a5957e81647af54016cc3b742353a3902aa141bf7b35becc88"}
+{"episode": 65491, "episode_seed": 2306902234, "split": "train", "timestamp": "2025-09-15T23:57:47.354504", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2306902234, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-54.24453676445186, 3.4697923432611546, -9.846897418318624])</answer>", "action": "('move', [-54.24453676445186, 3.4697923432611546, -9.846897418318624])", "reward": 0.0, "image": 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"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', [-54.24453676445186, 3.4697923432611546, -9.846897418318624])</answer>", "action": "('move', [-54.24453676445186, 3.4697923432611546, -9.846897418318624])", "reward": 0.0, "image": 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"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', [-54.24453676445186, 3.4697923432611546, -9.846897418318624])</answer>", "action": "('move', [-54.24453676445186, 3.4697923432611546, -9.846897418318624])", "reward": 0.0, "image": 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"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": "a2e2c76ec836fa9087209270157cbab1d54f41706a321d3be241638c4472d117"}
+{"episode": 65492, "episode_seed": 1012629684, "split": "train", "timestamp": "2025-09-15T23:57:47.960984", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1012629684, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-1.9103392294358992, -14.340768820012627, -25.459173706802943])</answer>", "action": "('move', [-1.9103392294358992, -14.340768820012627, -25.459173706802943])", "reward": 0.0, "image": 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"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', [-1.9103392294358992, -14.340768820012627, -25.459173706802943])</answer>", "action": "('move', [-1.9103392294358992, -14.340768820012627, -25.459173706802943])", "reward": 0.0, "image": 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"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', [-1.9103392294358992, -14.340768820012627, -25.459173706802943])</answer>", "action": "('move', [-1.9103392294358992, -14.340768820012627, -25.459173706802943])", "reward": 0.0, "image": 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"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": "65263d049f358afaa3e0bc73da5ccc0bab9b47f9f502118a63dbb5c252da8b9f"}
+{"episode": 65493, "episode_seed": 69505005, "split": "train", "timestamp": "2025-09-15T23:57:48.565560", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 69505005, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-0.7569737409107077, 4.084409682837667, -42.640765852609434])</answer>", "action": "('move', [-0.7569737409107077, 4.084409682837667, -42.640765852609434])", "reward": 0.0, "image": 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"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', [-0.7569737409107077, 4.084409682837667, -42.640765852609434])</answer>", "action": "('move', [-0.7569737409107077, 4.084409682837667, -42.640765852609434])", "reward": 0.0, "image": 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"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', [-0.7569737409107077, 4.084409682837667, -42.640765852609434])</answer>", "action": "('move', [-0.7569737409107077, 4.084409682837667, -42.640765852609434])", "reward": 0.0, "image": 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"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": "5e9e96cd87b179bc3bea4e97e9033b89f5613628b230f680e2ccf586d3ef1ade"}
+{"episode": 65494, "episode_seed": 3517141238, "split": "train", "timestamp": "2025-09-15T23:57:49.507896", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 3517141238, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [7.97345876046344, -1.0752742955023578, 39.00544607751747])</answer>", "action": "('move', [7.97345876046344, -1.0752742955023578, 39.00544607751747])", "reward": 0.0, "image": 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"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', [7.97345876046344, -1.0752742955023578, 39.00544607751747])</answer>", "action": "('move', [7.97345876046344, -1.0752742955023578, 39.00544607751747])", "reward": 0.0, "image": 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"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', [7.97345876046344, -1.0752742955023578, 39.00544607751747])</answer>", "action": "('move', [7.97345876046344, -1.0752742955023578, 39.00544607751747])", "reward": 0.0, "image": 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"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": "2937dd4e481b00f705f27fb4156e928ec70130143591e771d2ffdd37f58d1e20"}
+{"episode": 65495, "episode_seed": 2798712331, "split": "train", "timestamp": "2025-09-15T23:57:50.117315", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2798712331, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [35.9844637296251, -24.92429666079657, -35.272829699138256])</answer>", "action": "('move', [35.9844637296251, -24.92429666079657, -35.272829699138256])", "reward": 0.0, "image": 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"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', [35.9844637296251, -24.92429666079657, -35.272829699138256])</answer>", "action": "('move', [35.9844637296251, -24.92429666079657, -35.272829699138256])", "reward": 0.0, "image": 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"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', [35.9844637296251, -24.92429666079657, -35.272829699138256])</answer>", "action": "('move', [35.9844637296251, -24.92429666079657, -35.272829699138256])", "reward": 0.0, "image": 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"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": "8b52489db9b310a4f4aea73158bb28532580d20d908d4cc53208eb3a1114934c"}
+{"episode": 65496, "episode_seed": 2023914193, "split": "train", "timestamp": "2025-09-15T23:57:50.724636", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2023914193, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [7.192877951508106, 31.273955304445874, 18.717217201319112])</answer>", "action": "('move', [7.192877951508106, 31.273955304445874, 18.717217201319112])", "reward": 0.0, "image": 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"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', [7.192877951508106, 31.273955304445874, 18.717217201319112])</answer>", "action": "('move', [7.192877951508106, 31.273955304445874, 18.717217201319112])", "reward": 0.0, "image": 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"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', [7.192877951508106, 31.273955304445874, 18.717217201319112])</answer>", "action": "('move', [7.192877951508106, 31.273955304445874, 18.717217201319112])", "reward": 0.0, "image": 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"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": "5897541979c342f7be66023a9e6d2b6332d68ac64ed361a4ef6b70e84dd42bcb"}
+{"episode": 65497, "episode_seed": 2778250959, "split": "train", "timestamp": "2025-09-15T23:57:51.669064", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2778250959, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-46.213592025256474, -96.2997912329872, 24.93020371800436])</answer>", "action": "('move', [-46.213592025256474, -96.2997912329872, 24.93020371800436])", "reward": 0.0, "image": 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"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', [-46.213592025256474, -96.2997912329872, 24.93020371800436])</answer>", "action": "('move', [-46.213592025256474, -96.2997912329872, 24.93020371800436])", "reward": 0.0, "image": 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"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', [-46.213592025256474, -96.2997912329872, 24.93020371800436])</answer>", "action": "('move', [-46.213592025256474, -96.2997912329872, 24.93020371800436])", "reward": 0.0, "image": 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"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": "50064e18c89c3cfed770a7fb02d75781b517733aef4f6d4528f1562a016fdca5"}
+{"episode": 65498, "episode_seed": 2678890392, "split": "train", "timestamp": "2025-09-15T23:57:52.287514", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 2678890392, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [4.03018723094321, 6.065833469698998, -8.768285983596428])</answer>", "action": "('move', [4.03018723094321, 6.065833469698998, -8.768285983596428])", "reward": 0.0, "image": 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"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', [4.03018723094321, 6.065833469698998, -8.768285983596428])</answer>", "action": "('move', [4.03018723094321, 6.065833469698998, -8.768285983596428])", "reward": 0.0, "image": 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"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', [4.03018723094321, 6.065833469698998, -8.768285983596428])</answer>", "action": "('move', [4.03018723094321, 6.065833469698998, -8.768285983596428])", "reward": 0.0, "image": 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"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": "b02bdace9267c2c456cff53f5c455202b33098426905b69904c7dac6ba9229fb"}
+{"episode": 65499, "episode_seed": 1041900193, "split": "train", "timestamp": "2025-09-15T23:57:52.902168", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x14db964a8520>", "max_steps": 10, "abs_size": 336, "master_seed": 696, "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": 1041900193, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_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', [-4.493281681845801, -6.256078469188441, -53.024621713050045])</answer>", "action": "('move', [-4.493281681845801, -6.256078469188441, -53.024621713050045])", "reward": 0.0, "image": 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"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', [-4.493281681845801, -6.256078469188441, -53.024621713050045])</answer>", "action": "('move', [-4.493281681845801, -6.256078469188441, -53.024621713050045])", "reward": 0.0, "image": 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"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', [-4.493281681845801, -6.256078469188441, -53.024621713050045])</answer>", "action": "('move', [-4.493281681845801, -6.256078469188441, -53.024621713050045])", "reward": 0.0, "image": 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"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": "929d68b64bb583e1a907bbdf0402e7715c44ce1a83efe4d11b05206442ab99a8"}