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    "story_id": "mp-2026-10-17-005",
    "source_story_id": "tmp-story-coda-policy-data-coevolution",
    "edition_id": "mp-2026-10-17-morning-0099",
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    "section": "robotics",
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    "headline": "The Simulator Learns Which Failures to Rebuild",
    "slug": "the-simulator-learns-which-failures-to-rebuild",
    "dek": "CoDA shares a memory between the data engine and robot policy so new environments target diagnosed weaknesses.",
    "summary": "CoDA shares a memory between the data engine and robot policy so new environments target diagnosed weaknesses.",
    "body_text": "Physically valid simulated scenes are not necessarily useful training examples. CoDA stores environment programs, policy failures and learning progress in one heterogeneous memory. A vision-language critic turns validated diagnoses into new generation programs, while staged reward potentials supervise policy updates. Across four manipulation families, the authors report out-of-distribution success rising from 5.3 percent after supervised fine-tuning to 27.6 percent, 9.5 points above the strongest closed-loop baseline at the same generation budget. On LIBERO-Pro, fifteen rounds produced larger gains under position and instruction perturbations. The measurements remain simulator- and benchmark-specific.",
    "why_it_matters": "CoDA shares a memory between the data engine and robot policy so new environments target diagnosed weaknesses.",
    "limitations": [
      "Physically valid simulated scenes are not necessarily useful training examples.",
      "The measurements remain simulator- and benchmark-specific."
    ],
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    "first_published_at": "2026-10-17T09:00:00.000-04:00",
    "modified_at": "2026-10-17T09:00:00.000-04:00",
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        "qualification": "Physically valid simulated scenes are not necessarily useful training examples."
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    "tags": [
      "simulation",
      "robot policies",
      "data engines"
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  "sources": [
    {
      "source_id": "source-2026-10-17-005",
      "title": "arXiv preprint 2610.22362, original v1",
      "publisher": "arXiv",
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      "published_at": "2026-10-15T13:59:02.000-04:00",
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  "publisher": {
    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "The Simulator Learns Which Failures to Rebuild",
    "publisher": "The Machine Press",
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