diff --git "a/train/batch_000863.jsonl" "b/train/batch_000863.jsonl"
new file mode 100644--- /dev/null
+++ "b/train/batch_000863.jsonl"
@@ -0,0 +1,100 @@
+{"episode": 86300, "episode_seed": 401564872, "split": "train", "timestamp": "2025-09-16T00:21:13.520635", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 401564872, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.15332992204991583, -13.361146259355673, -14.941752582569668])</answer>", "action": "('move', [-0.15332992204991583, -13.361146259355673, -14.941752582569668])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.15332992204991583, -13.361146259355673, -14.941752582569668])</answer>", "action": "('move', [-0.15332992204991583, -13.361146259355673, -14.941752582569668])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.15332992204991583, -13.361146259355673, -14.941752582569668])</answer>", "action": "('move', [-0.15332992204991583, -13.361146259355673, -14.941752582569668])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "a540c87f6350ca3914defd9ac5ac54844a0d731cd83017246842b702d3296970"}
+{"episode": 86301, "episode_seed": 867010758, "split": "train", "timestamp": "2025-09-16T00:21:14.105826", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 867010758, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-120.41590869411068, -64.74286534976675, -59.32064944043077])</answer>", "action": "('move', [-120.41590869411068, -64.74286534976675, -59.32064944043077])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-120.41590869411068, -64.74286534976675, -59.32064944043077])</answer>", "action": "('move', [-120.41590869411068, -64.74286534976675, -59.32064944043077])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-120.41590869411068, -64.74286534976675, -59.32064944043077])</answer>", "action": "('move', [-120.41590869411068, -64.74286534976675, -59.32064944043077])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "e746b81e27ac16b8c6078d03bf732f8185bb5abd6c06b03aea6c368718054382"}
+{"episode": 86302, "episode_seed": 2644536633, "split": "train", "timestamp": "2025-09-16T00:21:14.693768", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2644536633, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [7.604729481387996, 44.25496442054443, 42.768039635644755])</answer>", "action": "('move', [7.604729481387996, 44.25496442054443, 42.768039635644755])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [7.604729481387996, 44.25496442054443, 42.768039635644755])</answer>", "action": "('move', [7.604729481387996, 44.25496442054443, 42.768039635644755])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [7.604729481387996, 44.25496442054443, 42.768039635644755])</answer>", "action": "('move', [7.604729481387996, 44.25496442054443, 42.768039635644755])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "f96e7cf0761c1c71e239cd117eb02ca2e5a4665dcec8ebaf58649ce1f800eae5"}
+{"episode": 86303, "episode_seed": 3116481074, "split": "train", "timestamp": "2025-09-16T00:21:15.601162", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3116481074, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-35.89686525370127, 18.657502256848243, 30.72414746540583])</answer>", "action": "('move', [-35.89686525370127, 18.657502256848243, 30.72414746540583])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-35.89686525370127, 18.657502256848243, 30.72414746540583])</answer>", "action": "('move', [-35.89686525370127, 18.657502256848243, 30.72414746540583])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-35.89686525370127, 18.657502256848243, 30.72414746540583])</answer>", "action": "('move', [-35.89686525370127, 18.657502256848243, 30.72414746540583])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "f69aca8d6d677659b0afa29b4f68358de64ff35f4c1ddf0a104faeb5afc5fa12"}
+{"episode": 86304, "episode_seed": 4005917034, "split": "train", "timestamp": "2025-09-16T00:21:16.189159", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4005917034, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [96.91263816843501, -12.277785269990835, 14.992874268665616])</answer>", "action": "('move', [96.91263816843501, -12.277785269990835, 14.992874268665616])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [96.91263816843501, -12.277785269990835, 14.992874268665616])</answer>", "action": "('move', [96.91263816843501, -12.277785269990835, 14.992874268665616])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [96.91263816843501, -12.277785269990835, 14.992874268665616])</answer>", "action": "('move', [96.91263816843501, -12.277785269990835, 14.992874268665616])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "065e369553d0208ef581ca99c2eb4c35e2508337c5257e9bd2a6ab7b46589728"}
+{"episode": 86305, "episode_seed": 2276520216, "split": "train", "timestamp": "2025-09-16T00:21:16.773711", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2276520216, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-9.880521409081634, -15.0246330182953, -27.38235859548152])</answer>", "action": "('move', [-9.880521409081634, -15.0246330182953, -27.38235859548152])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-9.880521409081634, -15.0246330182953, -27.38235859548152])</answer>", "action": "('move', [-9.880521409081634, -15.0246330182953, -27.38235859548152])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-9.880521409081634, -15.0246330182953, -27.38235859548152])</answer>", "action": "('move', [-9.880521409081634, -15.0246330182953, -27.38235859548152])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "c4dc0dc38d988b993545ba628afb10bacd48ac719c1080a3d575aa189cb9e556"}
+{"episode": 86306, "episode_seed": 1242648395, "split": "train", "timestamp": "2025-09-16T00:21:17.360783", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1242648395, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [1.9869723130464656, 0.709002588332448, 46.064008654779116])</answer>", "action": "('move', [1.9869723130464656, 0.709002588332448, 46.064008654779116])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [1.9869723130464656, 0.709002588332448, 46.064008654779116])</answer>", "action": "('move', [1.9869723130464656, 0.709002588332448, 46.064008654779116])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [1.9869723130464656, 0.709002588332448, 46.064008654779116])</answer>", "action": "('move', [1.9869723130464656, 0.709002588332448, 46.064008654779116])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "22c1486aa005b8589bb43dfe362ec9fd5dbca3b9ee13b6e5b621dfe40e36e403"}
+{"episode": 86307, "episode_seed": 1825220584, "split": "train", "timestamp": "2025-09-16T00:21:18.268430", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1825220584, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.6150181287594637, -1.2930122287167005, 30.23662542879718])</answer>", "action": "('move', [0.6150181287594637, -1.2930122287167005, 30.23662542879718])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.6150181287594637, -1.2930122287167005, 30.23662542879718])</answer>", "action": "('move', [0.6150181287594637, -1.2930122287167005, 30.23662542879718])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.6150181287594637, -1.2930122287167005, 30.23662542879718])</answer>", "action": "('move', [0.6150181287594637, -1.2930122287167005, 30.23662542879718])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "5c4c3fc2c180a7b41230850f0cb9e2e95b23f7a1f4f7ad369b603c5524170465"}
+{"episode": 86308, "episode_seed": 2326314267, "split": "train", "timestamp": "2025-09-16T00:21:18.854216", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2326314267, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.2504943083847446, -6.2740752389121, 19.664612638809587])</answer>", "action": "('move', [2.2504943083847446, -6.2740752389121, 19.664612638809587])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.2504943083847446, -6.2740752389121, 19.664612638809587])</answer>", "action": "('move', [2.2504943083847446, -6.2740752389121, 19.664612638809587])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.2504943083847446, -6.2740752389121, 19.664612638809587])</answer>", "action": "('move', [2.2504943083847446, -6.2740752389121, 19.664612638809587])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "3bb6c9a8809ab36359454a5df07b5a2135a35d89a9499c2b23d1182c50082784"}
+{"episode": 86309, "episode_seed": 1499131624, "split": "train", "timestamp": "2025-09-16T00:21:19.442357", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1499131624, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.14091463704707, -9.622964808139285, -22.99294990084557])</answer>", "action": "('move', [-2.14091463704707, -9.622964808139285, -22.99294990084557])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.14091463704707, -9.622964808139285, -22.99294990084557])</answer>", "action": "('move', [-2.14091463704707, -9.622964808139285, -22.99294990084557])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.14091463704707, -9.622964808139285, -22.99294990084557])</answer>", "action": "('move', [-2.14091463704707, -9.622964808139285, -22.99294990084557])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "6e1ff389380e46bf571020ec051ec899d15b904f878bfd386458851f36891026"}
+{"episode": 86310, "episode_seed": 3321348979, "split": "train", "timestamp": "2025-09-16T00:21:20.359858", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3321348979, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.813788239167804, -1.8612309765871464, -10.900259452264635])</answer>", "action": "('move', [-2.813788239167804, -1.8612309765871464, -10.900259452264635])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.813788239167804, -1.8612309765871464, -10.900259452264635])</answer>", "action": "('move', [-2.813788239167804, -1.8612309765871464, -10.900259452264635])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.813788239167804, -1.8612309765871464, -10.900259452264635])</answer>", "action": "('move', [-2.813788239167804, -1.8612309765871464, -10.900259452264635])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "400f9b0e3fc254d0eddd249ce46497c025e3860945285b2dfb7394a27eb15950"}
+{"episode": 86311, "episode_seed": 3173929373, "split": "train", "timestamp": "2025-09-16T00:21:20.947387", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3173929373, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [1.1718467276455689, -2.639020623479884, 42.64236216563626])</answer>", "action": "('move', [1.1718467276455689, -2.639020623479884, 42.64236216563626])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [1.1718467276455689, -2.639020623479884, 42.64236216563626])</answer>", "action": "('move', [1.1718467276455689, -2.639020623479884, 42.64236216563626])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [1.1718467276455689, -2.639020623479884, 42.64236216563626])</answer>", "action": "('move', [1.1718467276455689, -2.639020623479884, 42.64236216563626])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "b6bf993ba46416ab6d3984d874bae13fb0c0705ae53df515a7c0cb3c1fe9104b"}
+{"episode": 86312, "episode_seed": 783494260, "split": "train", "timestamp": "2025-09-16T00:21:21.529750", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 783494260, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.4244166801316847, -0.09186328573735497, 12.985747194588365])</answer>", "action": "('move', [2.4244166801316847, -0.09186328573735497, 12.985747194588365])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.4244166801316847, -0.09186328573735497, 12.985747194588365])</answer>", "action": "('move', [2.4244166801316847, -0.09186328573735497, 12.985747194588365])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.4244166801316847, -0.09186328573735497, 12.985747194588365])</answer>", "action": "('move', [2.4244166801316847, -0.09186328573735497, 12.985747194588365])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "8626174cfdd88cb9848063bf01d72bedfd32c39f521c50d321d64984756db509"}
+{"episode": 86313, "episode_seed": 2439451763, "split": "train", "timestamp": "2025-09-16T00:21:22.117108", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2439451763, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.8287106644683897, -4.190970065277118, 54.23450460419124])</answer>", "action": "('move', [0.8287106644683897, -4.190970065277118, 54.23450460419124])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.8287106644683897, -4.190970065277118, 54.23450460419124])</answer>", "action": "('move', [0.8287106644683897, -4.190970065277118, 54.23450460419124])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.8287106644683897, -4.190970065277118, 54.23450460419124])</answer>", "action": "('move', [0.8287106644683897, -4.190970065277118, 54.23450460419124])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "2bf9437d4b0bb01bfd7711a516287b7f98059d882b8efd94946f114e936ea2fc"}
+{"episode": 86314, "episode_seed": 313487559, "split": "train", "timestamp": "2025-09-16T00:21:23.019435", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 313487559, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.7853094940173299, 2.1927434052461967, 25.541829846292387])</answer>", "action": "('move', [-0.7853094940173299, 2.1927434052461967, 25.541829846292387])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.7853094940173299, 2.1927434052461967, 25.541829846292387])</answer>", "action": "('move', [-0.7853094940173299, 2.1927434052461967, 25.541829846292387])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.7853094940173299, 2.1927434052461967, 25.541829846292387])</answer>", "action": "('move', [-0.7853094940173299, 2.1927434052461967, 25.541829846292387])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "d7fd9fc2995a207c23efc90175bc0ab03d9175e3392126ccd29ab42c5706d65a"}
+{"episode": 86315, "episode_seed": 2912350247, "split": "train", "timestamp": "2025-09-16T00:21:23.604685", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2912350247, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-27.840995860686505, -26.25231229628136, -10.676201088600768])</answer>", "action": "('move', [-27.840995860686505, -26.25231229628136, -10.676201088600768])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-27.840995860686505, -26.25231229628136, -10.676201088600768])</answer>", "action": "('move', [-27.840995860686505, -26.25231229628136, -10.676201088600768])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-27.840995860686505, -26.25231229628136, -10.676201088600768])</answer>", "action": "('move', [-27.840995860686505, -26.25231229628136, -10.676201088600768])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "a791c3acc81309102a10cc1d7e57b93ac408a9f69659d9bb47d6265354fe7755"}
+{"episode": 86316, "episode_seed": 3107450078, "split": "train", "timestamp": "2025-09-16T00:21:24.190837", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3107450078, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-4.193722543812057, -4.234727626837739, 55.349556688452786])</answer>", "action": "('move', [-4.193722543812057, -4.234727626837739, 55.349556688452786])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-4.193722543812057, -4.234727626837739, 55.349556688452786])</answer>", "action": "('move', [-4.193722543812057, -4.234727626837739, 55.349556688452786])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-4.193722543812057, -4.234727626837739, 55.349556688452786])</answer>", "action": "('move', [-4.193722543812057, -4.234727626837739, 55.349556688452786])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "37884f849771dd83f2c982d55fbf6a6c146f582e423788215d4e7773e191eeef"}
+{"episode": 86317, "episode_seed": 1802939390, "split": "train", "timestamp": "2025-09-16T00:21:25.100748", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1802939390, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-9.379482918618368, -7.706276328482456, 33.1116618602771])</answer>", "action": "('move', [-9.379482918618368, -7.706276328482456, 33.1116618602771])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-9.379482918618368, -7.706276328482456, 33.1116618602771])</answer>", "action": "('move', [-9.379482918618368, -7.706276328482456, 33.1116618602771])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-9.379482918618368, -7.706276328482456, 33.1116618602771])</answer>", "action": "('move', [-9.379482918618368, -7.706276328482456, 33.1116618602771])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "4bd9eba8f7f51ee0888b55939cef840b5a397b66fa169157119e3f03b88ef24a"}
+{"episode": 86318, "episode_seed": 2742546266, "split": "train", "timestamp": "2025-09-16T00:21:25.688389", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2742546266, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.6103801933843136, 1.6415565559632121, -49.1937151398508])</answer>", "action": "('move', [-2.6103801933843136, 1.6415565559632121, -49.1937151398508])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.6103801933843136, 1.6415565559632121, -49.1937151398508])</answer>", "action": "('move', [-2.6103801933843136, 1.6415565559632121, -49.1937151398508])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.6103801933843136, 1.6415565559632121, -49.1937151398508])</answer>", "action": "('move', [-2.6103801933843136, 1.6415565559632121, -49.1937151398508])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "dd860d9d31acce8199279c4b07b43080967b112c29e16634c2cd096890b511bb"}
+{"episode": 86319, "episode_seed": 888363232, "split": "train", "timestamp": "2025-09-16T00:21:26.274335", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 888363232, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-16.567191531565772, 21.18308688982994, 15.019387946107116])</answer>", "action": "('move', [-16.567191531565772, 21.18308688982994, 15.019387946107116])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-16.567191531565772, 21.18308688982994, 15.019387946107116])</answer>", "action": "('move', [-16.567191531565772, 21.18308688982994, 15.019387946107116])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-16.567191531565772, 21.18308688982994, 15.019387946107116])</answer>", "action": "('move', [-16.567191531565772, 21.18308688982994, 15.019387946107116])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "fa89008f81a8db86e0ce93c9e2a57e04b5847d842e1848c4beb821c8a506917c"}
+{"episode": 86320, "episode_seed": 3997817301, "split": "train", "timestamp": "2025-09-16T00:21:26.868650", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3997817301, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-19.182337183301986, -5.4284737022477145, -29.337830503208465])</answer>", "action": "('move', [-19.182337183301986, -5.4284737022477145, -29.337830503208465])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-19.182337183301986, -5.4284737022477145, -29.337830503208465])</answer>", "action": "('move', [-19.182337183301986, -5.4284737022477145, -29.337830503208465])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-19.182337183301986, -5.4284737022477145, -29.337830503208465])</answer>", "action": "('move', [-19.182337183301986, -5.4284737022477145, -29.337830503208465])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "efc2c793eb4146c94d06e5605c2ad05f105d54eeb7da859ec9ebdef8022a4a2f"}
+{"episode": 86321, "episode_seed": 3388837610, "split": "train", "timestamp": "2025-09-16T00:21:27.797105", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3388837610, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-10.588546290995604, 4.418942502949049, 28.2239876148541])</answer>", "action": "('move', [-10.588546290995604, 4.418942502949049, 28.2239876148541])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-10.588546290995604, 4.418942502949049, 28.2239876148541])</answer>", "action": "('move', [-10.588546290995604, 4.418942502949049, 28.2239876148541])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-10.588546290995604, 4.418942502949049, 28.2239876148541])</answer>", "action": "('move', [-10.588546290995604, 4.418942502949049, 28.2239876148541])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "bb35bb3bba882cc4131491f1582177103d63b122755baff05cd71769f8c09e00"}
+{"episode": 86322, "episode_seed": 2623575438, "split": "train", "timestamp": "2025-09-16T00:21:28.384713", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2623575438, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-6.563817844097135, 1.668739850549535, -6.560365906293479])</answer>", "action": "('move', [-6.563817844097135, 1.668739850549535, -6.560365906293479])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-6.563817844097135, 1.668739850549535, -6.560365906293479])</answer>", "action": "('move', [-6.563817844097135, 1.668739850549535, -6.560365906293479])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-6.563817844097135, 1.668739850549535, -6.560365906293479])</answer>", "action": "('move', [-6.563817844097135, 1.668739850549535, -6.560365906293479])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "3062ae744aa7e234f311786a722d699920e12fff66b730ccfa5da2460a94700b"}
+{"episode": 86323, "episode_seed": 1949511802, "split": "train", "timestamp": "2025-09-16T00:21:28.968055", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1949511802, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [27.278216509389978, 8.315554010537678, -1.9503983361374821])</answer>", "action": "('move', [27.278216509389978, 8.315554010537678, -1.9503983361374821])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [27.278216509389978, 8.315554010537678, -1.9503983361374821])</answer>", "action": "('move', [27.278216509389978, 8.315554010537678, -1.9503983361374821])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [27.278216509389978, 8.315554010537678, -1.9503983361374821])</answer>", "action": "('move', [27.278216509389978, 8.315554010537678, -1.9503983361374821])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "03ce8edbd0fc65186723b4b4beb6be56ee06835374b139428896077022130029"}
+{"episode": 86324, "episode_seed": 2495293566, "split": "train", "timestamp": "2025-09-16T00:21:29.876073", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2495293566, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [118.5023639302732, 11.557824807519575, 39.991827343235364])</answer>", "action": "('move', [118.5023639302732, 11.557824807519575, 39.991827343235364])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [118.5023639302732, 11.557824807519575, 39.991827343235364])</answer>", "action": "('move', [118.5023639302732, 11.557824807519575, 39.991827343235364])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [118.5023639302732, 11.557824807519575, 39.991827343235364])</answer>", "action": "('move', [118.5023639302732, 11.557824807519575, 39.991827343235364])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "9555d9d0b702b5987bdf45b9c55e8720b115074a752f890942c449e47f67240e"}
+{"episode": 86325, "episode_seed": 1925635456, "split": "train", "timestamp": "2025-09-16T00:21:30.463044", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1925635456, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [6.114886999758311, -2.1976557439831796, -26.964716714974184])</answer>", "action": "('move', [6.114886999758311, -2.1976557439831796, -26.964716714974184])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [6.114886999758311, -2.1976557439831796, -26.964716714974184])</answer>", "action": "('move', [6.114886999758311, -2.1976557439831796, -26.964716714974184])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [6.114886999758311, -2.1976557439831796, -26.964716714974184])</answer>", "action": "('move', [6.114886999758311, -2.1976557439831796, -26.964716714974184])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "3e0ddcf8bcf1aa5fd9dfb34e373b10b41325577249f992041d65c8206877d799"}
+{"episode": 86326, "episode_seed": 2052597496, "split": "train", "timestamp": "2025-09-16T00:21:31.052132", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2052597496, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.22617310537484, -0.5147964705394622, -49.368902292220774])</answer>", "action": "('move', [-2.22617310537484, -0.5147964705394622, -49.368902292220774])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.22617310537484, -0.5147964705394622, -49.368902292220774])</answer>", "action": "('move', [-2.22617310537484, -0.5147964705394622, -49.368902292220774])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.22617310537484, -0.5147964705394622, -49.368902292220774])</answer>", "action": "('move', [-2.22617310537484, -0.5147964705394622, -49.368902292220774])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "5168c53dff90013570bacbebb27b9dcb9cabead91634718cfb61512eab060da3"}
+{"episode": 86327, "episode_seed": 2669321446, "split": "train", "timestamp": "2025-09-16T00:21:31.638903", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2669321446, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.0540777608733833, -1.8809680372672108, 41.353492045143206])</answer>", "action": "('move', [-3.0540777608733833, -1.8809680372672108, 41.353492045143206])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.0540777608733833, -1.8809680372672108, 41.353492045143206])</answer>", "action": "('move', [-3.0540777608733833, -1.8809680372672108, 41.353492045143206])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.0540777608733833, -1.8809680372672108, 41.353492045143206])</answer>", "action": "('move', [-3.0540777608733833, -1.8809680372672108, 41.353492045143206])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "f089cd0e420ef239d6e3dcbbe5ddb814127cf2330a19c756779671e95bcb38c9"}
+{"episode": 86328, "episode_seed": 2620617518, "split": "train", "timestamp": "2025-09-16T00:21:32.551239", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2620617518, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [28.647977285456992, 11.946696882106934, 23.82698495624898])</answer>", "action": "('move', [28.647977285456992, 11.946696882106934, 23.82698495624898])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [28.647977285456992, 11.946696882106934, 23.82698495624898])</answer>", "action": "('move', [28.647977285456992, 11.946696882106934, 23.82698495624898])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [28.647977285456992, 11.946696882106934, 23.82698495624898])</answer>", "action": "('move', [28.647977285456992, 11.946696882106934, 23.82698495624898])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "f716292084f6bcd0eff89ec59c8bbb6942e71ea4963cecb69045a9b00dd7ec04"}
+{"episode": 86329, "episode_seed": 184618983, "split": "train", "timestamp": "2025-09-16T00:21:33.139205", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 184618983, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.9276972818505412, -6.47268032273486, -17.963568919637975])</answer>", "action": "('move', [0.9276972818505412, -6.47268032273486, -17.963568919637975])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.9276972818505412, -6.47268032273486, -17.963568919637975])</answer>", "action": "('move', [0.9276972818505412, -6.47268032273486, -17.963568919637975])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.9276972818505412, -6.47268032273486, -17.963568919637975])</answer>", "action": "('move', [0.9276972818505412, -6.47268032273486, -17.963568919637975])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "5ddbe79fdb5aee48a21d36bf4dc35d8d81c2a39d153581d97ae0c696cc017a98"}
+{"episode": 86330, "episode_seed": 24326358, "split": "train", "timestamp": "2025-09-16T00:21:33.723051", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 24326358, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.1744446613070703, -1.3997276228902258, 15.299927414337683])</answer>", "action": "('move', [-3.1744446613070703, -1.3997276228902258, 15.299927414337683])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.1744446613070703, -1.3997276228902258, 15.299927414337683])</answer>", "action": "('move', [-3.1744446613070703, -1.3997276228902258, 15.299927414337683])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.1744446613070703, -1.3997276228902258, 15.299927414337683])</answer>", "action": "('move', [-3.1744446613070703, -1.3997276228902258, 15.299927414337683])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "819a6506a295377c299f1d7b7c87769da2d9e9878622caf14608d58d14562eef"}
+{"episode": 86331, "episode_seed": 154632825, "split": "train", "timestamp": "2025-09-16T00:21:34.637444", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 154632825, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.3196792492046296, 32.546897596937875, -38.65954542013314])</answer>", "action": "('move', [3.3196792492046296, 32.546897596937875, -38.65954542013314])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.3196792492046296, 32.546897596937875, -38.65954542013314])</answer>", "action": "('move', [3.3196792492046296, 32.546897596937875, -38.65954542013314])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.3196792492046296, 32.546897596937875, -38.65954542013314])</answer>", "action": "('move', [3.3196792492046296, 32.546897596937875, -38.65954542013314])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "c96f3ae9fc44dde77f2ca5f04756d48900899218fb7f6ed2d0846d15ed766646"}
+{"episode": 86332, "episode_seed": 1923713533, "split": "train", "timestamp": "2025-09-16T00:21:35.227172", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1923713533, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-4.493115244286487, -3.3488618139637385, 36.18077017185457])</answer>", "action": "('move', [-4.493115244286487, -3.3488618139637385, 36.18077017185457])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-4.493115244286487, -3.3488618139637385, 36.18077017185457])</answer>", "action": "('move', [-4.493115244286487, -3.3488618139637385, 36.18077017185457])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-4.493115244286487, -3.3488618139637385, 36.18077017185457])</answer>", "action": "('move', [-4.493115244286487, -3.3488618139637385, 36.18077017185457])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "3c1010c5c7517e645ef5b3184ba0123bae71bb6d8e011ded4df81cd1599ba62d"}
+{"episode": 86333, "episode_seed": 707488652, "split": "train", "timestamp": "2025-09-16T00:21:35.814657", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 707488652, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.994127480136721, 2.1283311910248517, -52.967174753515735])</answer>", "action": "('move', [-1.994127480136721, 2.1283311910248517, -52.967174753515735])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.994127480136721, 2.1283311910248517, -52.967174753515735])</answer>", "action": "('move', [-1.994127480136721, 2.1283311910248517, -52.967174753515735])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.994127480136721, 2.1283311910248517, -52.967174753515735])</answer>", "action": "('move', [-1.994127480136721, 2.1283311910248517, -52.967174753515735])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "5e4a7c854856f99baad3a1f5ac01cb7f3f0893e8c5262c15949b889dc68f644e"}
+{"episode": 86334, "episode_seed": 99697086, "split": "train", "timestamp": "2025-09-16T00:21:36.399649", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 99697086, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [4.06535779436878, 4.9954410996553325, -24.372432483839248])</answer>", "action": "('move', [4.06535779436878, 4.9954410996553325, -24.372432483839248])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [4.06535779436878, 4.9954410996553325, -24.372432483839248])</answer>", "action": "('move', [4.06535779436878, 4.9954410996553325, -24.372432483839248])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [4.06535779436878, 4.9954410996553325, -24.372432483839248])</answer>", "action": "('move', [4.06535779436878, 4.9954410996553325, -24.372432483839248])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "406f21809a81a81a8d3718a4d0793d24b1ff67021a2801cab0b61791255be5d0"}
+{"episode": 86335, "episode_seed": 92842467, "split": "train", "timestamp": "2025-09-16T00:21:37.314704", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 92842467, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.167615126776293, -3.0815364624276813, 30.243050461764067])</answer>", "action": "('move', [2.167615126776293, -3.0815364624276813, 30.243050461764067])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.167615126776293, -3.0815364624276813, 30.243050461764067])</answer>", "action": "('move', [2.167615126776293, -3.0815364624276813, 30.243050461764067])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.167615126776293, -3.0815364624276813, 30.243050461764067])</answer>", "action": "('move', [2.167615126776293, -3.0815364624276813, 30.243050461764067])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "a4ef7e71bec354015b25aff72be1c83edc63ab188fa6ce3e83a9bc9f5b600fb5"}
+{"episode": 86336, "episode_seed": 1301408905, "split": "train", "timestamp": "2025-09-16T00:21:37.902947", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1301408905, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.3629358171250716, -1.8937522951686618, 23.7311621896766])</answer>", "action": "('move', [-1.3629358171250716, -1.8937522951686618, 23.7311621896766])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.3629358171250716, -1.8937522951686618, 23.7311621896766])</answer>", "action": "('move', [-1.3629358171250716, -1.8937522951686618, 23.7311621896766])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.3629358171250716, -1.8937522951686618, 23.7311621896766])</answer>", "action": "('move', [-1.3629358171250716, -1.8937522951686618, 23.7311621896766])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "b78a6698701f6df99dc95211920045569548d6df691e4c21ecb5fee5a0ef0926"}
+{"episode": 86337, "episode_seed": 2909964888, "split": "train", "timestamp": "2025-09-16T00:21:38.493986", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2909964888, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-124.20453330471581, 55.029755824109934, -7.501876772585642])</answer>", "action": "('move', [-124.20453330471581, 55.029755824109934, -7.501876772585642])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-124.20453330471581, 55.029755824109934, -7.501876772585642])</answer>", "action": "('move', [-124.20453330471581, 55.029755824109934, -7.501876772585642])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-124.20453330471581, 55.029755824109934, -7.501876772585642])</answer>", "action": "('move', [-124.20453330471581, 55.029755824109934, -7.501876772585642])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "5eece8ca63268ba6e3c1804d9b2d42c3f57d31cff0ace751d80088b98cfa4a4c"}
+{"episode": 86338, "episode_seed": 3577558139, "split": "train", "timestamp": "2025-09-16T00:21:39.078970", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3577558139, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.1175441753106354, 1.063776743248438, -59.25732707260966])</answer>", "action": "('move', [2.1175441753106354, 1.063776743248438, -59.25732707260966])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.1175441753106354, 1.063776743248438, -59.25732707260966])</answer>", "action": "('move', [2.1175441753106354, 1.063776743248438, -59.25732707260966])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.1175441753106354, 1.063776743248438, -59.25732707260966])</answer>", "action": "('move', [2.1175441753106354, 1.063776743248438, -59.25732707260966])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "492db0b3f228f98577df2ee8e19341044565a8b35f75ae8df0738f2e1b069c80"}
+{"episode": 86339, "episode_seed": 2847869078, "split": "train", "timestamp": "2025-09-16T00:21:39.990464", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2847869078, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.7652783178811071, 1.3515436722025254, -20.39004806782177])</answer>", "action": "('move', [-0.7652783178811071, 1.3515436722025254, -20.39004806782177])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.7652783178811071, 1.3515436722025254, -20.39004806782177])</answer>", "action": "('move', [-0.7652783178811071, 1.3515436722025254, -20.39004806782177])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.7652783178811071, 1.3515436722025254, -20.39004806782177])</answer>", "action": "('move', [-0.7652783178811071, 1.3515436722025254, -20.39004806782177])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "e6878a244fee6cc5015d333922f6866a35f1a18649bb5fa898b377fb19c015a6"}
+{"episode": 86340, "episode_seed": 181962957, "split": "train", "timestamp": "2025-09-16T00:21:40.577958", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 181962957, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.0820612398891623, 2.041335026846359, -0.2640101965397624])</answer>", "action": "('move', [-2.0820612398891623, 2.041335026846359, -0.2640101965397624])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.0820612398891623, 2.041335026846359, -0.2640101965397624])</answer>", "action": "('move', [-2.0820612398891623, 2.041335026846359, -0.2640101965397624])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.0820612398891623, 2.041335026846359, -0.2640101965397624])</answer>", "action": "('move', [-2.0820612398891623, 2.041335026846359, -0.2640101965397624])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "730634f1b76992868a85d885c1594bfff8c9e90f504c2bf012be21890c0335c2"}
+{"episode": 86341, "episode_seed": 799294040, "split": "train", "timestamp": "2025-09-16T00:21:41.161472", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 799294040, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-5.057990487697382, -16.893724921479365, -32.94525258182072])</answer>", "action": "('move', [-5.057990487697382, -16.893724921479365, -32.94525258182072])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-5.057990487697382, -16.893724921479365, -32.94525258182072])</answer>", "action": "('move', [-5.057990487697382, -16.893724921479365, -32.94525258182072])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-5.057990487697382, -16.893724921479365, -32.94525258182072])</answer>", "action": "('move', [-5.057990487697382, -16.893724921479365, -32.94525258182072])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "9af682308d7a8a53aa1740f471a31d5d6dcd7324a0b726a6fcd19fb82ac2999a"}
+{"episode": 86342, "episode_seed": 3450797564, "split": "train", "timestamp": "2025-09-16T00:21:42.071142", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3450797564, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-10.639376114315223, -11.038261942846619, -47.95281774066853])</answer>", "action": "('move', [-10.639376114315223, -11.038261942846619, -47.95281774066853])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-10.639376114315223, -11.038261942846619, -47.95281774066853])</answer>", "action": "('move', [-10.639376114315223, -11.038261942846619, -47.95281774066853])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-10.639376114315223, -11.038261942846619, -47.95281774066853])</answer>", "action": "('move', [-10.639376114315223, -11.038261942846619, -47.95281774066853])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "074b4c40d2d619bef9f53380e28c8a424371f8afbda7f61853cdfd5e2018e9fa"}
+{"episode": 86343, "episode_seed": 2845414908, "split": "train", "timestamp": "2025-09-16T00:21:42.659603", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2845414908, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.6126026684490835, -20.09528249833843, 20.059377664867423])</answer>", "action": "('move', [-3.6126026684490835, -20.09528249833843, 20.059377664867423])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.6126026684490835, -20.09528249833843, 20.059377664867423])</answer>", "action": "('move', [-3.6126026684490835, -20.09528249833843, 20.059377664867423])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.6126026684490835, -20.09528249833843, 20.059377664867423])</answer>", "action": "('move', [-3.6126026684490835, -20.09528249833843, 20.059377664867423])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "e8c76da25e6d131f1b3dff6dca8fe6275260d9db9dfb1a1220b397d70f2c03e9"}
+{"episode": 86344, "episode_seed": 4287582774, "split": "train", "timestamp": "2025-09-16T00:21:43.244483", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4287582774, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.28184626214669, -3.4468112605996652, 6.492786948823361])</answer>", "action": "('move', [3.28184626214669, -3.4468112605996652, 6.492786948823361])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.28184626214669, -3.4468112605996652, 6.492786948823361])</answer>", "action": "('move', [3.28184626214669, -3.4468112605996652, 6.492786948823361])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.28184626214669, -3.4468112605996652, 6.492786948823361])</answer>", "action": "('move', [3.28184626214669, -3.4468112605996652, 6.492786948823361])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "930a28ffb46782f34a56ac9df3bb67da0116735c0e9e0da4bcc27182602d56a6"}
+{"episode": 86345, "episode_seed": 1962290103, "split": "train", "timestamp": "2025-09-16T00:21:43.829098", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1962290103, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.7551540271333175, 2.7755675146977894, -44.686056301271186])</answer>", "action": "('move', [2.7551540271333175, 2.7755675146977894, -44.686056301271186])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.7551540271333175, 2.7755675146977894, -44.686056301271186])</answer>", "action": "('move', [2.7551540271333175, 2.7755675146977894, -44.686056301271186])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.7551540271333175, 2.7755675146977894, -44.686056301271186])</answer>", "action": "('move', [2.7551540271333175, 2.7755675146977894, -44.686056301271186])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "ff1e9699876e35c242180cd56534f5f3b3dcb0b890f2d75c69e63940e682bd5f"}
+{"episode": 86346, "episode_seed": 2473080954, "split": "train", "timestamp": "2025-09-16T00:21:44.737461", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2473080954, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.6460535572322716, -1.0089012775819821, 30.17095152043392])</answer>", "action": "('move', [3.6460535572322716, -1.0089012775819821, 30.17095152043392])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.6460535572322716, -1.0089012775819821, 30.17095152043392])</answer>", "action": "('move', [3.6460535572322716, -1.0089012775819821, 30.17095152043392])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.6460535572322716, -1.0089012775819821, 30.17095152043392])</answer>", "action": "('move', [3.6460535572322716, -1.0089012775819821, 30.17095152043392])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "fbb3d4153f1e0f203a3510ebfb615855ed13b15c184eb0c0a64a95d6a07458bc"}
+{"episode": 86347, "episode_seed": 356973247, "split": "train", "timestamp": "2025-09-16T00:21:45.323036", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 356973247, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [20.65374883521862, -9.024699091837828, -45.09604420784228])</answer>", "action": "('move', [20.65374883521862, -9.024699091837828, -45.09604420784228])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [20.65374883521862, -9.024699091837828, -45.09604420784228])</answer>", "action": "('move', [20.65374883521862, -9.024699091837828, -45.09604420784228])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [20.65374883521862, -9.024699091837828, -45.09604420784228])</answer>", "action": "('move', [20.65374883521862, -9.024699091837828, -45.09604420784228])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "1ed15ab96d844449350d1af09aa6e970ffc397afd963fbfe5e705689c7d739f3"}
+{"episode": 86348, "episode_seed": 397479565, "split": "train", "timestamp": "2025-09-16T00:21:45.907656", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 397479565, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [24.487803333059375, 1.3208537025148777, -12.069033819085822])</answer>", "action": "('move', [24.487803333059375, 1.3208537025148777, -12.069033819085822])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [24.487803333059375, 1.3208537025148777, -12.069033819085822])</answer>", "action": "('move', [24.487803333059375, 1.3208537025148777, -12.069033819085822])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [24.487803333059375, 1.3208537025148777, -12.069033819085822])</answer>", "action": "('move', [24.487803333059375, 1.3208537025148777, -12.069033819085822])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "93cd50b5e67a3399a2625b76048238e5d63dbde89da3537b76a58f9da6131242"}
+{"episode": 86349, "episode_seed": 3055596317, "split": "train", "timestamp": "2025-09-16T00:21:46.812179", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3055596317, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-28.89969592751335, -3.3792347475438684, -55.11718419652584])</answer>", "action": "('move', [-28.89969592751335, -3.3792347475438684, -55.11718419652584])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-28.89969592751335, -3.3792347475438684, -55.11718419652584])</answer>", "action": "('move', [-28.89969592751335, -3.3792347475438684, -55.11718419652584])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-28.89969592751335, -3.3792347475438684, -55.11718419652584])</answer>", "action": "('move', [-28.89969592751335, -3.3792347475438684, -55.11718419652584])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "f04f82215efddeaa409bc77f6863c1034cb240611936d06114d4d82ce355cf17"}
+{"episode": 86350, "episode_seed": 2192211559, "split": "train", "timestamp": "2025-09-16T00:21:47.402606", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2192211559, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [35.562257859346374, -25.826804516482387, -38.44265335071277])</answer>", "action": "('move', [35.562257859346374, -25.826804516482387, -38.44265335071277])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [35.562257859346374, -25.826804516482387, -38.44265335071277])</answer>", "action": "('move', [35.562257859346374, -25.826804516482387, -38.44265335071277])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [35.562257859346374, -25.826804516482387, -38.44265335071277])</answer>", "action": "('move', [35.562257859346374, -25.826804516482387, -38.44265335071277])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "e39562193315708d59d7ab664fd42f9956c8d3b0127f9330d13b45d766d3222b"}
+{"episode": 86351, "episode_seed": 4063577576, "split": "train", "timestamp": "2025-09-16T00:21:47.987393", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4063577576, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.9128850297746465, 0.5507914935571129, -52.2862765674557])</answer>", "action": "('move', [3.9128850297746465, 0.5507914935571129, -52.2862765674557])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.9128850297746465, 0.5507914935571129, -52.2862765674557])</answer>", "action": "('move', [3.9128850297746465, 0.5507914935571129, -52.2862765674557])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.9128850297746465, 0.5507914935571129, -52.2862765674557])</answer>", "action": "('move', [3.9128850297746465, 0.5507914935571129, -52.2862765674557])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "03ba9c3e5f13fe016f2f76a93e58676fc78e2f9791e84d654563a7c48d92abef"}
+{"episode": 86352, "episode_seed": 3690272969, "split": "train", "timestamp": "2025-09-16T00:21:48.574638", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3690272969, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.079471995655908, 4.700371762102988, 8.251510584574882])</answer>", "action": "('move', [3.079471995655908, 4.700371762102988, 8.251510584574882])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.079471995655908, 4.700371762102988, 8.251510584574882])</answer>", "action": "('move', [3.079471995655908, 4.700371762102988, 8.251510584574882])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.079471995655908, 4.700371762102988, 8.251510584574882])</answer>", "action": "('move', [3.079471995655908, 4.700371762102988, 8.251510584574882])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "cd78d8cdc27a96bbe8d9ecea457bea09d86eac2f2541a55bfe984d360c1ab1c6"}
+{"episode": 86353, "episode_seed": 2049357959, "split": "train", "timestamp": "2025-09-16T00:21:49.477327", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2049357959, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.43310992677540067, -4.704494854614017, 28.702219291482947])</answer>", "action": "('move', [0.43310992677540067, -4.704494854614017, 28.702219291482947])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.43310992677540067, -4.704494854614017, 28.702219291482947])</answer>", "action": "('move', [0.43310992677540067, -4.704494854614017, 28.702219291482947])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.43310992677540067, -4.704494854614017, 28.702219291482947])</answer>", "action": "('move', [0.43310992677540067, -4.704494854614017, 28.702219291482947])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "db92c54e0fafe208010cf8db5ab45448246e2a05e6f94ed38ee9b763805f3032"}
+{"episode": 86354, "episode_seed": 2463927186, "split": "train", "timestamp": "2025-09-16T00:21:50.062800", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2463927186, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-39.595759489343386, -23.1549133737888, -27.847900294626452])</answer>", "action": "('move', [-39.595759489343386, -23.1549133737888, -27.847900294626452])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-39.595759489343386, -23.1549133737888, -27.847900294626452])</answer>", "action": "('move', [-39.595759489343386, -23.1549133737888, -27.847900294626452])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-39.595759489343386, -23.1549133737888, -27.847900294626452])</answer>", "action": "('move', [-39.595759489343386, -23.1549133737888, -27.847900294626452])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "2d488e857a1bb914fa0ec3cffecbd3432456a1708d4fdf712eff4a82486e667f"}
+{"episode": 86355, "episode_seed": 2021146296, "split": "train", "timestamp": "2025-09-16T00:21:50.647854", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2021146296, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [10.829038638910646, 20.54485106761404, 39.0708633161286])</answer>", "action": "('move', [10.829038638910646, 20.54485106761404, 39.0708633161286])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [10.829038638910646, 20.54485106761404, 39.0708633161286])</answer>", "action": "('move', [10.829038638910646, 20.54485106761404, 39.0708633161286])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [10.829038638910646, 20.54485106761404, 39.0708633161286])</answer>", "action": "('move', [10.829038638910646, 20.54485106761404, 39.0708633161286])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "aad3d36684dfc69494d1e7d006a09b629df9c926664a4b26673c8ec4c5ed1cd5"}
+{"episode": 86356, "episode_seed": 926467363, "split": "train", "timestamp": "2025-09-16T00:21:51.546500", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 926467363, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [7.719098844730172, -2.7149141891443214, -32.52219058188539])</answer>", "action": "('move', [7.719098844730172, -2.7149141891443214, -32.52219058188539])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [7.719098844730172, -2.7149141891443214, -32.52219058188539])</answer>", "action": "('move', [7.719098844730172, -2.7149141891443214, -32.52219058188539])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [7.719098844730172, -2.7149141891443214, -32.52219058188539])</answer>", "action": "('move', [7.719098844730172, -2.7149141891443214, -32.52219058188539])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "2740bb48ca555a1f4ceb4a8d9418d8bb844e5d7d4676659f90de15d8307d7257"}
+{"episode": 86357, "episode_seed": 2565964483, "split": "train", "timestamp": "2025-09-16T00:21:52.136176", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2565964483, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-8.320007081760627, -12.049394608956598, -18.400004202165395])</answer>", "action": "('move', [-8.320007081760627, -12.049394608956598, -18.400004202165395])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-8.320007081760627, -12.049394608956598, -18.400004202165395])</answer>", "action": "('move', [-8.320007081760627, -12.049394608956598, -18.400004202165395])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-8.320007081760627, -12.049394608956598, -18.400004202165395])</answer>", "action": "('move', [-8.320007081760627, -12.049394608956598, -18.400004202165395])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "6a8c6cb1363940fe72b3bb939653ecdb168b98cb1412bb9642ceceff91953a35"}
+{"episode": 86358, "episode_seed": 369770892, "split": "train", "timestamp": "2025-09-16T00:21:52.727411", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 369770892, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.3677641826724827, -1.5961744779982026, 4.160967933856123])</answer>", "action": "('move', [-3.3677641826724827, -1.5961744779982026, 4.160967933856123])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.3677641826724827, -1.5961744779982026, 4.160967933856123])</answer>", "action": "('move', [-3.3677641826724827, -1.5961744779982026, 4.160967933856123])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.3677641826724827, -1.5961744779982026, 4.160967933856123])</answer>", "action": "('move', [-3.3677641826724827, -1.5961744779982026, 4.160967933856123])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "a641959ec7d266ed72a14a4347e0c5b7e525f63b7d003e18a85abaedf5a71189"}
+{"episode": 86359, "episode_seed": 159630976, "split": "train", "timestamp": "2025-09-16T00:21:53.322427", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 159630976, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.5392445314622714, 41.57855660112392, -59.07988464187466])</answer>", "action": "('move', [-1.5392445314622714, 41.57855660112392, -59.07988464187466])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.5392445314622714, 41.57855660112392, -59.07988464187466])</answer>", "action": "('move', [-1.5392445314622714, 41.57855660112392, -59.07988464187466])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.5392445314622714, 41.57855660112392, -59.07988464187466])</answer>", "action": "('move', [-1.5392445314622714, 41.57855660112392, -59.07988464187466])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "b01d1867bac36495a752db0d503758dd8bfba44ec9aeb9329d7b78496508a803"}
+{"episode": 86360, "episode_seed": 1939516303, "split": "train", "timestamp": "2025-09-16T00:21:54.248289", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1939516303, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.9427212248399286, -1.0419221635133455, 11.446162447097246])</answer>", "action": "('move', [2.9427212248399286, -1.0419221635133455, 11.446162447097246])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.9427212248399286, -1.0419221635133455, 11.446162447097246])</answer>", "action": "('move', [2.9427212248399286, -1.0419221635133455, 11.446162447097246])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [2.9427212248399286, -1.0419221635133455, 11.446162447097246])</answer>", "action": "('move', [2.9427212248399286, -1.0419221635133455, 11.446162447097246])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "acfa4fd4fb4ef3ed15ecf8d570e1303b040e529cef2b6cf077fac19057d2050c"}
+{"episode": 86361, "episode_seed": 1743000202, "split": "train", "timestamp": "2025-09-16T00:21:54.851049", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1743000202, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.7911117600350104, 2.3429459254232614, 58.398739613279076])</answer>", "action": "('move', [-3.7911117600350104, 2.3429459254232614, 58.398739613279076])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.7911117600350104, 2.3429459254232614, 58.398739613279076])</answer>", "action": "('move', [-3.7911117600350104, 2.3429459254232614, 58.398739613279076])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.7911117600350104, 2.3429459254232614, 58.398739613279076])</answer>", "action": "('move', [-3.7911117600350104, 2.3429459254232614, 58.398739613279076])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "6d2376ffc611e4420b8d8f9ed3d6b960b47c354c05f0c195c485001d3f1e756f"}
+{"episode": 86362, "episode_seed": 1186208362, "split": "train", "timestamp": "2025-09-16T00:21:55.443619", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1186208362, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.4272510270637664, 25.013320120512414, -12.188743698229208])</answer>", "action": "('move', [3.4272510270637664, 25.013320120512414, -12.188743698229208])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.4272510270637664, 25.013320120512414, -12.188743698229208])</answer>", "action": "('move', [3.4272510270637664, 25.013320120512414, -12.188743698229208])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.4272510270637664, 25.013320120512414, -12.188743698229208])</answer>", "action": "('move', [3.4272510270637664, 25.013320120512414, -12.188743698229208])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "81861805d2fc96a5aecb172002664a5fe10ab7e30b795f46856b01e8eb95b742"}
+{"episode": 86363, "episode_seed": 199974137, "split": "train", "timestamp": "2025-09-16T00:21:56.377045", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 199974137, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.6358740471664708, 3.346556700832771, 13.54382954007059])</answer>", "action": "('move', [-1.6358740471664708, 3.346556700832771, 13.54382954007059])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.6358740471664708, 3.346556700832771, 13.54382954007059])</answer>", "action": "('move', [-1.6358740471664708, 3.346556700832771, 13.54382954007059])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.6358740471664708, 3.346556700832771, 13.54382954007059])</answer>", "action": "('move', [-1.6358740471664708, 3.346556700832771, 13.54382954007059])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "f61bcb15a1aaf901537361041b0bc960a5a0beb93b32c58e6eb94b9f4ffb5806"}
+{"episode": 86364, "episode_seed": 3847789809, "split": "train", "timestamp": "2025-09-16T00:21:56.973499", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3847789809, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-8.436100799726018, 14.33237215916646, -24.02410432955429])</answer>", "action": "('move', [-8.436100799726018, 14.33237215916646, -24.02410432955429])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-8.436100799726018, 14.33237215916646, -24.02410432955429])</answer>", "action": "('move', [-8.436100799726018, 14.33237215916646, -24.02410432955429])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-8.436100799726018, 14.33237215916646, -24.02410432955429])</answer>", "action": "('move', [-8.436100799726018, 14.33237215916646, -24.02410432955429])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "7dda408de711fc75636968ae5ab0a8618646030a524b707ade41edcf396c99f7"}
+{"episode": 86365, "episode_seed": 586444780, "split": "train", "timestamp": "2025-09-16T00:21:57.570705", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 586444780, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [4.585701362992618, 3.2233127694228685, 35.64870001387493])</answer>", "action": "('move', [4.585701362992618, 3.2233127694228685, 35.64870001387493])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [4.585701362992618, 3.2233127694228685, 35.64870001387493])</answer>", "action": "('move', [4.585701362992618, 3.2233127694228685, 35.64870001387493])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [4.585701362992618, 3.2233127694228685, 35.64870001387493])</answer>", "action": "('move', [4.585701362992618, 3.2233127694228685, 35.64870001387493])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "7a8252f89da3cb7b14d6d41d8bea27555ae85e9c83077eadca8ed5388865989b"}
+{"episode": 86366, "episode_seed": 3858978980, "split": "train", "timestamp": "2025-09-16T00:21:58.164413", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3858978980, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-86.75382103165906, 32.30462484569342, -49.30422289253453])</answer>", "action": "('move', [-86.75382103165906, 32.30462484569342, -49.30422289253453])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-86.75382103165906, 32.30462484569342, -49.30422289253453])</answer>", "action": "('move', [-86.75382103165906, 32.30462484569342, -49.30422289253453])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-86.75382103165906, 32.30462484569342, -49.30422289253453])</answer>", "action": "('move', [-86.75382103165906, 32.30462484569342, -49.30422289253453])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "dacd16e5fe7704e0f785768992c9231bc29cdf72e8ff12ec84c6ad22ee5fe07b"}
+{"episode": 86367, "episode_seed": 3940805014, "split": "train", "timestamp": "2025-09-16T00:21:59.099080", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3940805014, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.07966349673580086, -11.094114351222244, -1.833997929674581])</answer>", "action": "('move', [0.07966349673580086, -11.094114351222244, -1.833997929674581])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.07966349673580086, -11.094114351222244, -1.833997929674581])</answer>", "action": "('move', [0.07966349673580086, -11.094114351222244, -1.833997929674581])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.07966349673580086, -11.094114351222244, -1.833997929674581])</answer>", "action": "('move', [0.07966349673580086, -11.094114351222244, -1.833997929674581])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "9158e1bd645df15ba9b9254294e9d61c0ffc6939a3517348410cfaa0c47ab4a5"}
+{"episode": 86368, "episode_seed": 3860017689, "split": "train", "timestamp": "2025-09-16T00:21:59.696507", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3860017689, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.7748821488642683, 1.823836104734606, 5.470900903738472])</answer>", "action": "('move', [3.7748821488642683, 1.823836104734606, 5.470900903738472])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.7748821488642683, 1.823836104734606, 5.470900903738472])</answer>", "action": "('move', [3.7748821488642683, 1.823836104734606, 5.470900903738472])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.7748821488642683, 1.823836104734606, 5.470900903738472])</answer>", "action": "('move', [3.7748821488642683, 1.823836104734606, 5.470900903738472])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "f942ba79b167567fe69aee75078a1e9d0619260321d675927e600d76c00337ae"}
+{"episode": 86369, "episode_seed": 1583310258, "split": "train", "timestamp": "2025-09-16T00:22:00.289418", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1583310258, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-21.846331751510856, -50.65260154933177, -19.54580144190415])</answer>", "action": "('move', [-21.846331751510856, -50.65260154933177, -19.54580144190415])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-21.846331751510856, -50.65260154933177, -19.54580144190415])</answer>", "action": "('move', [-21.846331751510856, -50.65260154933177, -19.54580144190415])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-21.846331751510856, -50.65260154933177, -19.54580144190415])</answer>", "action": "('move', [-21.846331751510856, -50.65260154933177, -19.54580144190415])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "5f98a8bd375ee9d3e9b608bf9a4b50d6d99fc45f6eddcfc926621e3e4bd70c03"}
+{"episode": 86370, "episode_seed": 3120821637, "split": "train", "timestamp": "2025-09-16T00:22:01.219923", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3120821637, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-6.1153728512749765, -11.083426017025834, 51.92922872481034])</answer>", "action": "('move', [-6.1153728512749765, -11.083426017025834, 51.92922872481034])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-6.1153728512749765, -11.083426017025834, 51.92922872481034])</answer>", "action": "('move', [-6.1153728512749765, -11.083426017025834, 51.92922872481034])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-6.1153728512749765, -11.083426017025834, 51.92922872481034])</answer>", "action": "('move', [-6.1153728512749765, -11.083426017025834, 51.92922872481034])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "42662be76ce82b4e31cd5ea30026578ac8ad7f073a9e7ee60add3785151f9695"}
+{"episode": 86371, "episode_seed": 424418477, "split": "train", "timestamp": "2025-09-16T00:22:01.816027", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 424418477, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.5972297694454621, -3.208715817180036, -14.634230089144191])</answer>", "action": "('move', [0.5972297694454621, -3.208715817180036, -14.634230089144191])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.5972297694454621, -3.208715817180036, -14.634230089144191])</answer>", "action": "('move', [0.5972297694454621, -3.208715817180036, -14.634230089144191])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.5972297694454621, -3.208715817180036, -14.634230089144191])</answer>", "action": "('move', [0.5972297694454621, -3.208715817180036, -14.634230089144191])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "9a7592633bb24d9d69fb08ea1502e0622e1f42a5cc9be6f76df1fb3256e6a801"}
+{"episode": 86372, "episode_seed": 1415011793, "split": "train", "timestamp": "2025-09-16T00:22:02.417100", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1415011793, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.978853641485576, -0.4824400503827982, -41.56694253542474])</answer>", "action": "('move', [-1.978853641485576, -0.4824400503827982, -41.56694253542474])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.978853641485576, -0.4824400503827982, -41.56694253542474])</answer>", "action": "('move', [-1.978853641485576, -0.4824400503827982, -41.56694253542474])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.978853641485576, -0.4824400503827982, -41.56694253542474])</answer>", "action": "('move', [-1.978853641485576, -0.4824400503827982, -41.56694253542474])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "e03074a41ebcc4bdcdb4390a9bd50a57298a096839229a13b62fc719fdabd318"}
+{"episode": 86373, "episode_seed": 2109862415, "split": "train", "timestamp": "2025-09-16T00:22:03.000343", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2109862415, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.34594086199745505, -6.028086964784724, 20.48106829120937])</answer>", "action": "('move', [-0.34594086199745505, -6.028086964784724, 20.48106829120937])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.34594086199745505, -6.028086964784724, 20.48106829120937])</answer>", "action": "('move', [-0.34594086199745505, -6.028086964784724, 20.48106829120937])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.34594086199745505, -6.028086964784724, 20.48106829120937])</answer>", "action": "('move', [-0.34594086199745505, -6.028086964784724, 20.48106829120937])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "e1fdf08f5dd9d595bf71d6486b199a325b907a5f7f2babd18a79365dae14d21b"}
+{"episode": 86374, "episode_seed": 2170380428, "split": "train", "timestamp": "2025-09-16T00:22:03.907594", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2170380428, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.2149321605861453, -2.998003315892786, -11.615795030785923])</answer>", "action": "('move', [-0.2149321605861453, -2.998003315892786, -11.615795030785923])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.2149321605861453, -2.998003315892786, -11.615795030785923])</answer>", "action": "('move', [-0.2149321605861453, -2.998003315892786, -11.615795030785923])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.2149321605861453, -2.998003315892786, -11.615795030785923])</answer>", "action": "('move', [-0.2149321605861453, -2.998003315892786, -11.615795030785923])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "2132833f5a61c51bf775423d95e38936fe623773fd1feaddfb73c41fd775c03f"}
+{"episode": 86375, "episode_seed": 3129856236, "split": "train", "timestamp": "2025-09-16T00:22:04.499113", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3129856236, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.681922989718496, 9.348462864043725, -32.94557264779662])</answer>", "action": "('move', [-3.681922989718496, 9.348462864043725, -32.94557264779662])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.681922989718496, 9.348462864043725, -32.94557264779662])</answer>", "action": "('move', [-3.681922989718496, 9.348462864043725, -32.94557264779662])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-3.681922989718496, 9.348462864043725, -32.94557264779662])</answer>", "action": "('move', [-3.681922989718496, 9.348462864043725, -32.94557264779662])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "c2c88231a0bd5842451cc3d76447e6e20a665e6f61b5c81b662c1082c7430abc"}
+{"episode": 86376, "episode_seed": 2481572032, "split": "train", "timestamp": "2025-09-16T00:22:05.085360", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2481572032, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.454278506841043, 0.6808631938417239, 42.7390041851318])</answer>", "action": "('move', [-1.454278506841043, 0.6808631938417239, 42.7390041851318])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.454278506841043, 0.6808631938417239, 42.7390041851318])</answer>", "action": "('move', [-1.454278506841043, 0.6808631938417239, 42.7390041851318])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.454278506841043, 0.6808631938417239, 42.7390041851318])</answer>", "action": "('move', [-1.454278506841043, 0.6808631938417239, 42.7390041851318])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "f09b314928cd5fd8d3a3e1ba83e56d271075ba3a8f14d1b0e8fc67bbc40d249d"}
+{"episode": 86377, "episode_seed": 10454180, "split": "train", "timestamp": "2025-09-16T00:22:05.992657", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 10454180, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [14.947747840633584, 8.160387432768067, 42.918967121572074])</answer>", "action": "('move', [14.947747840633584, 8.160387432768067, 42.918967121572074])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [14.947747840633584, 8.160387432768067, 42.918967121572074])</answer>", "action": "('move', [14.947747840633584, 8.160387432768067, 42.918967121572074])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [14.947747840633584, 8.160387432768067, 42.918967121572074])</answer>", "action": "('move', [14.947747840633584, 8.160387432768067, 42.918967121572074])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "8ef95882d9d967adb4133310679eab48080e389675634acfd5089c9f270a37f2"}
+{"episode": 86378, "episode_seed": 3185282183, "split": "train", "timestamp": "2025-09-16T00:22:06.580001", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3185282183, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [37.06988274698019, -52.16470410238882, 45.99977866604394])</answer>", "action": "('move', [37.06988274698019, -52.16470410238882, 45.99977866604394])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [37.06988274698019, -52.16470410238882, 45.99977866604394])</answer>", "action": "('move', [37.06988274698019, -52.16470410238882, 45.99977866604394])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [37.06988274698019, -52.16470410238882, 45.99977866604394])</answer>", "action": "('move', [37.06988274698019, -52.16470410238882, 45.99977866604394])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "3b08154069e479d0afd97f0ba0ec4af26832cabd5d25f4b6178ed7977bdc04ef"}
+{"episode": 86379, "episode_seed": 2524031710, "split": "train", "timestamp": "2025-09-16T00:22:07.170763", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2524031710, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-5.448993188225928, 6.5541088841107245, -1.9912782811032155])</answer>", "action": "('move', [-5.448993188225928, 6.5541088841107245, -1.9912782811032155])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-5.448993188225928, 6.5541088841107245, -1.9912782811032155])</answer>", "action": "('move', [-5.448993188225928, 6.5541088841107245, -1.9912782811032155])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-5.448993188225928, 6.5541088841107245, -1.9912782811032155])</answer>", "action": "('move', [-5.448993188225928, 6.5541088841107245, -1.9912782811032155])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "06b0ac08175fb42a3bbd42c3d19f2fae8381107bb219f3228e97208d009bed34"}
+{"episode": 86380, "episode_seed": 3379539363, "split": "train", "timestamp": "2025-09-16T00:22:07.755736", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3379539363, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-16.271752172360838, -29.284981735494995, -25.413557725221477])</answer>", "action": "('move', [-16.271752172360838, -29.284981735494995, -25.413557725221477])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-16.271752172360838, -29.284981735494995, -25.413557725221477])</answer>", "action": "('move', [-16.271752172360838, -29.284981735494995, -25.413557725221477])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-16.271752172360838, -29.284981735494995, -25.413557725221477])</answer>", "action": "('move', [-16.271752172360838, -29.284981735494995, -25.413557725221477])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "f1a368a4205b2257f938359104afc52a362d1e73c43a468ce65e966ceb076d30"}
+{"episode": 86381, "episode_seed": 828652134, "split": "train", "timestamp": "2025-09-16T00:22:08.684266", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 828652134, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [6.596826951095419, -1.8654660900461753, -27.42197229483037])</answer>", "action": "('move', [6.596826951095419, -1.8654660900461753, -27.42197229483037])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [6.596826951095419, -1.8654660900461753, -27.42197229483037])</answer>", "action": "('move', [6.596826951095419, -1.8654660900461753, -27.42197229483037])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [6.596826951095419, -1.8654660900461753, -27.42197229483037])</answer>", "action": "('move', [6.596826951095419, -1.8654660900461753, -27.42197229483037])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "eee031cd53b4e30098d474f5d1c7d49a19862b1b7ecd20313188412215425f56"}
+{"episode": 86382, "episode_seed": 4242453183, "split": "train", "timestamp": "2025-09-16T00:22:09.272011", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4242453183, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [1.5450722100504863, 2.3364978012822086, 27.159786171711534])</answer>", "action": "('move', [1.5450722100504863, 2.3364978012822086, 27.159786171711534])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [1.5450722100504863, 2.3364978012822086, 27.159786171711534])</answer>", "action": "('move', [1.5450722100504863, 2.3364978012822086, 27.159786171711534])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [1.5450722100504863, 2.3364978012822086, 27.159786171711534])</answer>", "action": "('move', [1.5450722100504863, 2.3364978012822086, 27.159786171711534])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "b41d5127b426fa37484cb0c1308ba979817ea374080c7d2b2200e7c69a23c293"}
+{"episode": 86383, "episode_seed": 3548357046, "split": "train", "timestamp": "2025-09-16T00:22:09.861161", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3548357046, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-34.46973893916073, 14.851094039553637, 54.7830796703538])</answer>", "action": "('move', [-34.46973893916073, 14.851094039553637, 54.7830796703538])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-34.46973893916073, 14.851094039553637, 54.7830796703538])</answer>", "action": "('move', [-34.46973893916073, 14.851094039553637, 54.7830796703538])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-34.46973893916073, 14.851094039553637, 54.7830796703538])</answer>", "action": "('move', [-34.46973893916073, 14.851094039553637, 54.7830796703538])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "8b731e8d601a600dbd3d551c7fd3645b2c571c792d507adf923d523cf9b619fc"}
+{"episode": 86384, "episode_seed": 3918009781, "split": "train", "timestamp": "2025-09-16T00:22:10.445926", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3918009781, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-24.798741765278013, 6.092334527112359, 14.247800352951193])</answer>", "action": "('move', [-24.798741765278013, 6.092334527112359, 14.247800352951193])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-24.798741765278013, 6.092334527112359, 14.247800352951193])</answer>", "action": "('move', [-24.798741765278013, 6.092334527112359, 14.247800352951193])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-24.798741765278013, 6.092334527112359, 14.247800352951193])</answer>", "action": "('move', [-24.798741765278013, 6.092334527112359, 14.247800352951193])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "d17f22bd370fd4f8bf5a062e63ad0a3b6e91a0fc978d3066179721d4b39b3158"}
+{"episode": 86385, "episode_seed": 3163748523, "split": "train", "timestamp": "2025-09-16T00:22:11.363196", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3163748523, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-8.262465262227272, -6.6583066585469455, 57.288826527619904])</answer>", "action": "('move', [-8.262465262227272, -6.6583066585469455, 57.288826527619904])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-8.262465262227272, -6.6583066585469455, 57.288826527619904])</answer>", "action": "('move', [-8.262465262227272, -6.6583066585469455, 57.288826527619904])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-8.262465262227272, -6.6583066585469455, 57.288826527619904])</answer>", "action": "('move', [-8.262465262227272, -6.6583066585469455, 57.288826527619904])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "9a2a3474e79fade9fc852733f70ef6c4575aaaaa25da85c797d986b8bea1563f"}
+{"episode": 86386, "episode_seed": 72941788, "split": "train", "timestamp": "2025-09-16T00:22:11.950972", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 72941788, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.01594684385471073, 3.1157727815344636, 17.156936461496674])</answer>", "action": "('move', [-0.01594684385471073, 3.1157727815344636, 17.156936461496674])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.01594684385471073, 3.1157727815344636, 17.156936461496674])</answer>", "action": "('move', [-0.01594684385471073, 3.1157727815344636, 17.156936461496674])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-0.01594684385471073, 3.1157727815344636, 17.156936461496674])</answer>", "action": "('move', [-0.01594684385471073, 3.1157727815344636, 17.156936461496674])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "f0a04d664686d0bc0fded35211c15893ba3ffc30981c9e9891d05c710fb0d5d3"}
+{"episode": 86387, "episode_seed": 4260031976, "split": "train", "timestamp": "2025-09-16T00:22:12.537707", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 4260031976, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [5.151268083081752, 2.5488129707821967, 50.5280006324629])</answer>", "action": "('move', [5.151268083081752, 2.5488129707821967, 50.5280006324629])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [5.151268083081752, 2.5488129707821967, 50.5280006324629])</answer>", "action": "('move', [5.151268083081752, 2.5488129707821967, 50.5280006324629])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [5.151268083081752, 2.5488129707821967, 50.5280006324629])</answer>", "action": "('move', [5.151268083081752, 2.5488129707821967, 50.5280006324629])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "4a08783d32cb2c620f35147d5cc05ef91adacd1057932b2084faa62bd2ce91a9"}
+{"episode": 86388, "episode_seed": 3080822321, "split": "train", "timestamp": "2025-09-16T00:22:13.454604", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3080822321, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.2817485949104086, -0.2688061992738863, 44.136900522661186])</answer>", "action": "('move', [-1.2817485949104086, -0.2688061992738863, 44.136900522661186])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.2817485949104086, -0.2688061992738863, 44.136900522661186])</answer>", "action": "('move', [-1.2817485949104086, -0.2688061992738863, 44.136900522661186])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.2817485949104086, -0.2688061992738863, 44.136900522661186])</answer>", "action": "('move', [-1.2817485949104086, -0.2688061992738863, 44.136900522661186])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "23871ed270c13898626e33decc10fdae3ae14c23ad2f3dd768fc02cc11754116"}
+{"episode": 86389, "episode_seed": 611518216, "split": "train", "timestamp": "2025-09-16T00:22:14.042372", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 611518216, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-47.685331686516925, 105.53957122846099, -56.381355299618065])</answer>", "action": "('move', [-47.685331686516925, 105.53957122846099, -56.381355299618065])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-47.685331686516925, 105.53957122846099, -56.381355299618065])</answer>", "action": "('move', [-47.685331686516925, 105.53957122846099, -56.381355299618065])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-47.685331686516925, 105.53957122846099, -56.381355299618065])</answer>", "action": "('move', [-47.685331686516925, 105.53957122846099, -56.381355299618065])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "91018089cdbd325b1857f6cc148c574c3cc865639c63c9b0b7c32489bcbff6ce"}
+{"episode": 86390, "episode_seed": 2556882072, "split": "train", "timestamp": "2025-09-16T00:22:14.631167", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2556882072, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.9148228654483983, 24.55380553119893, 51.577580622932715])</answer>", "action": "('move', [3.9148228654483983, 24.55380553119893, 51.577580622932715])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.9148228654483983, 24.55380553119893, 51.577580622932715])</answer>", "action": "('move', [3.9148228654483983, 24.55380553119893, 51.577580622932715])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [3.9148228654483983, 24.55380553119893, 51.577580622932715])</answer>", "action": "('move', [3.9148228654483983, 24.55380553119893, 51.577580622932715])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "8fcad3652417995c8c5bf6e4cdd66f4dfc48569cc4842d52cdf00446a1f45e52"}
+{"episode": 86391, "episode_seed": 972130534, "split": "train", "timestamp": "2025-09-16T00:22:15.214540", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 972130534, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.958788978667567, 1.1848631954377205, 54.240363579857046])</answer>", "action": "('move', [-2.958788978667567, 1.1848631954377205, 54.240363579857046])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.958788978667567, 1.1848631954377205, 54.240363579857046])</answer>", "action": "('move', [-2.958788978667567, 1.1848631954377205, 54.240363579857046])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.958788978667567, 1.1848631954377205, 54.240363579857046])</answer>", "action": "('move', [-2.958788978667567, 1.1848631954377205, 54.240363579857046])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "2da63ab2730365e38a18f7973dc486d2520e221b4710c59c6a7bd577c9d4ce50"}
+{"episode": 86392, "episode_seed": 1115032091, "split": "train", "timestamp": "2025-09-16T00:22:16.121890", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1115032091, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-6.53249192476576, -8.855912341451505, 30.751680708949625])</answer>", "action": "('move', [-6.53249192476576, -8.855912341451505, 30.751680708949625])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-6.53249192476576, -8.855912341451505, 30.751680708949625])</answer>", "action": "('move', [-6.53249192476576, -8.855912341451505, 30.751680708949625])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-6.53249192476576, -8.855912341451505, 30.751680708949625])</answer>", "action": "('move', [-6.53249192476576, -8.855912341451505, 30.751680708949625])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "be45c75df6d4ad55ec3ed461f52b2fc6db9d449b43e5fc331dbb842173390ef1"}
+{"episode": 86393, "episode_seed": 1841826984, "split": "train", "timestamp": "2025-09-16T00:22:16.706214", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 1841826984, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.9947241115490133, -3.068641327214951, -33.845113742755814])</answer>", "action": "('move', [-1.9947241115490133, -3.068641327214951, -33.845113742755814])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.9947241115490133, -3.068641327214951, -33.845113742755814])</answer>", "action": "('move', [-1.9947241115490133, -3.068641327214951, -33.845113742755814])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-1.9947241115490133, -3.068641327214951, -33.845113742755814])</answer>", "action": "('move', [-1.9947241115490133, -3.068641327214951, -33.845113742755814])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "fb41b72bc53002bacc159b4cb5dcc6dad17f9e74a71b97f48d8020c3ecfcf0f3"}
+{"episode": 86394, "episode_seed": 2136231667, "split": "train", "timestamp": "2025-09-16T00:22:17.294263", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 2136231667, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.769108719429481, -3.9739606370620204, 17.271253880739625])</answer>", "action": "('move', [-2.769108719429481, -3.9739606370620204, 17.271253880739625])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.769108719429481, -3.9739606370620204, 17.271253880739625])</answer>", "action": "('move', [-2.769108719429481, -3.9739606370620204, 17.271253880739625])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.769108719429481, -3.9739606370620204, 17.271253880739625])</answer>", "action": "('move', [-2.769108719429481, -3.9739606370620204, 17.271253880739625])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "33b299c29988bb3314633623e022229bfa0be8c975634bca286054d5b8ab2e37"}
+{"episode": 86395, "episode_seed": 3282960457, "split": "train", "timestamp": "2025-09-16T00:22:18.191297", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3282960457, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.46318114803418337, 0.875401116881977, 18.76998989158031])</answer>", "action": "('move', [0.46318114803418337, 0.875401116881977, 18.76998989158031])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.46318114803418337, 0.875401116881977, 18.76998989158031])</answer>", "action": "('move', [0.46318114803418337, 0.875401116881977, 18.76998989158031])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [0.46318114803418337, 0.875401116881977, 18.76998989158031])</answer>", "action": "('move', [0.46318114803418337, 0.875401116881977, 18.76998989158031])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "e9b8a5ff2e63219967a60b83b35ffeef6d0e419806248731954c7aa9bbccff80"}
+{"episode": 86396, "episode_seed": 3647564117, "split": "train", "timestamp": "2025-09-16T00:22:18.780311", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 3647564117, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [19.277719734382355, -9.164996874546258, -29.055234487070987])</answer>", "action": "('move', [19.277719734382355, -9.164996874546258, -29.055234487070987])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [19.277719734382355, -9.164996874546258, -29.055234487070987])</answer>", "action": "('move', [19.277719734382355, -9.164996874546258, -29.055234487070987])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [19.277719734382355, -9.164996874546258, -29.055234487070987])</answer>", "action": "('move', [19.277719734382355, -9.164996874546258, -29.055234487070987])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "8f6e5bbbaeb9c045fdc4e7ea24938265f1b0b6a07522df938ecf4cf7aa887443"}
+{"episode": 86397, "episode_seed": 62369363, "split": "train", "timestamp": "2025-09-16T00:22:19.366378", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 62369363, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.8900907047946536, -1.9722043476325821, -52.95967094224599])</answer>", "action": "('move', [-2.8900907047946536, -1.9722043476325821, -52.95967094224599])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.8900907047946536, -1.9722043476325821, -52.95967094224599])</answer>", "action": "('move', [-2.8900907047946536, -1.9722043476325821, -52.95967094224599])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-2.8900907047946536, -1.9722043476325821, -52.95967094224599])</answer>", "action": "('move', [-2.8900907047946536, -1.9722043476325821, -52.95967094224599])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "d10f04b2094f94c8e5b66face63162b9b9284f699aac563dc47e084bf45da5bd"}
+{"episode": 86398, "episode_seed": 870674791, "split": "train", "timestamp": "2025-09-16T00:22:19.957053", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 870674791, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [9.77408125367658, -45.21556379526936, -41.95591844069545])</answer>", "action": "('move', [9.77408125367658, -45.21556379526936, -41.95591844069545])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [9.77408125367658, -45.21556379526936, -41.95591844069545])</answer>", "action": "('move', [9.77408125367658, -45.21556379526936, -41.95591844069545])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [9.77408125367658, -45.21556379526936, -41.95591844069545])</answer>", "action": "('move', [9.77408125367658, -45.21556379526936, -41.95591844069545])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "cf8d7211e4c95900d597ce2bdb933978abb2c4098b770f4d87b46c9af6741085"}
+{"episode": 86399, "episode_seed": 801878988, "split": "train", "timestamp": "2025-09-16T00:22:20.860213", "batch_dir": null, "init_args": {"env_repr": "<OrderEnforcing<MatchMoveStickRotationEnv<match_move/MatchRotation-v0>>>", "vlm_repr": "<gymnasium.vlm.groundtruth_interactor.MockVLM object at 0x1511f09bb7c0>", "max_steps": 10, "abs_size": 336, "master_seed": 905, "max_long_side": 336, "interactor_type": "GroundtruthInteractor", "groundtruth_strategy": null, "groundtruth_kwargs": {"num_steps": 3}, "enable_reasoning": true, "reasoner_class": null}, "run_args": {"render": false, "seed": 801878988, "verbose": false, "save_verbose": false, "elicit_verbalization": true, "repeat_instructions": false, "include_env_feedback": true, "num_interaction_history": 0, "widget_log": false, "autosave_path": null, "save_history_at_episode_end": false}, "stats": {"step": 4, "reward": 1.0, "terminated": true, "truncated": false}, "history": [{"step": 0, "prompt": "You see a blue stick and a red target stick on a canvas. Your goal is to move and rotate the blue stick to match the red target stick. Unit moves are in an *unknown* scale (could be tiny or huge).\n\nAvailable actions:\n1. 'move': Translate by dx,dy units (any real number) and rotate by d\u03b8\u00b0 (any real number). Format: `('move', [dx, dy, d\u03b8])`\n2. 'stop': Submit your current stick as final. You win (+1) if your stick is within 5px and 10\u00b0 of the target. Format: `('stop', 'stop')`\n\nPlease respond with exactly one action and its arguments in the specified format. For example:\n- Move right by 2 units and rotate 45\u00b0: `('move', [2, 0, 45])`\n- Move diagonally and rotate: `('move', [1.5, -0.8, 90])`\n- Finalize the stick position: `('stop', 'stop')`\n\nVerbalize your reasoning in <think>\u2026</think> and give the final result in <answer>\u2026</answer>.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-87.18731133311081, -91.97456840111033, 18.086717567854794])</answer>", "action": "('move', [-87.18731133311081, -91.97456840111033, 18.086717567854794])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 1, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-87.18731133311081, -91.97456840111033, 18.086717567854794])</answer>", "action": "('move', [-87.18731133311081, -91.97456840111033, 18.086717567854794])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 2, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('move', [-87.18731133311081, -91.97456840111033, 18.086717567854794])</answer>", "action": "('move', [-87.18731133311081, -91.97456840111033, 18.086717567854794])", "reward": 0.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}, {"step": 3, "prompt": "Environment feedback: Action executed successfully.", "vlm_output": "Based on the current state, I need to take the optimal action to solve this task.<answer>('stop', 'stop')</answer>", "action": "('stop', 'stop')", "reward": 1.0, "image": 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"info": {"env_feedback": "Action executed successfully."}, "think": "Based on the current state, I need to take the optimal action to solve this task."}], "extra_state": null, "hash": "272ca2524db8f59ec7ac5fc67b6a2eb13bd4574fc28a283ddca2d2335a64e291"}