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    "headline": "The Robot Learns From the State Its Mistake Left Behind",
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    "dek": "A recovery benchmark and online training method reward both progress and verified recovery after manipulation errors.",
    "summary": "A recovery benchmark and online training method reward both progress and verified recovery after manipulation errors.",
    "body_text": "Execution errors leave embodied agents in states that a clean demonstration never visits. ERR-Env supplies feedback on progress and errors, while ERR-Bench evaluates nominal, intermediate and error states. Training a compact 2-billion-parameter model with action-grouped turn-level reinforcement learning improved strict error-free success by 10.25 percentage points and recovery rate by 22.95 points. The result is benchmark evidence, not proof of open-world recovery.",
    "why_it_matters": "A recovery benchmark and online training method reward both progress and verified recovery after manipulation errors.",
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      "The result is benchmark evidence, not proof of open-world recovery."
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    "first_published_at": "2026-10-21T09:00:00.000-04:00",
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        "qualification": "The result is benchmark evidence, not proof of open-world recovery."
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    "tags": [
      "robotics",
      "replanning",
      "reinforcement learning"
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      "title": "arXiv preprint 2610.27003, original v1",
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    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
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    "title": "The Robot Learns From the State Its Mistake Left Behind",
    "publisher": "The Machine Press",
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