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    "story_id": "mp-2026-11-08-007",
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    "edition_id": "mp-2026-11-08-morning-0121",
    "edition_url": "https://themachinepress.com/edition/2026-11-08",
    "position": 7,
    "story_type": "dispatch",
    "section": "robotics",
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    "headline": "The Robot Keeps the Controller and Relearns the View",
    "slug": "the-robot-keeps-the-controller-and-relearns-the-view",
    "dek": "A frozen action decoder transfers privileged reinforcement-learning behavior into visual manipulation with fewer demonstrations.",
    "summary": "A frozen action decoder transfers privileged reinforcement-learning behavior into visual manipulation with fewer demonstrations.",
    "body_text": "Latent RL Prior first distills simulated reinforcement-learning rollouts into a flow-matching action decoder conditioned on proprioception and latent task context. Visual adaptation freezes that decoder and trains only an image-to-context interface, with no privileged labels or latent targets. Across three simulated tasks, the method beat full-parameter fine-tuning in the low-data regime and surpassed full-data baselines on door lifting and bimanual assembly using ten and five times fewer demonstrations respectively. The evidence is simulation-based, but it isolates a practical route for reusing control knowledge.",
    "why_it_matters": "A frozen action decoder transfers privileged reinforcement-learning behavior into visual manipulation with fewer demonstrations.",
    "limitations": [
      "Visual adaptation freezes that decoder and trains only an image-to-context interface, with no privileged labels or latent targets."
    ],
    "importance": 9,
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    "first_published_at": "2026-11-08T09:00:00.000-05:00",
    "modified_at": "2026-11-08T09:00:00.000-05:00",
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        "text": "A frozen action decoder transfers privileged reinforcement-learning behavior into visual manipulation with fewer demonstrations.",
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        "qualification": "Visual adaptation freezes that decoder and trains only an image-to-context interface, with no privileged labels or latent targets."
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    "source_ids": [
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    "tags": [
      "dexterous manipulation",
      "reinforcement learning",
      "visual policies"
    ],
    "image_url": null,
    "corrections": []
  },
  "sources": [
    {
      "source_id": "source-2026-11-08-007",
      "title": "arXiv preprint 2611.08458, original v1",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2611.08458v1",
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      "published_at": "2026-11-05T13:50:06.000-05:00",
      "accessed_at": "2026-11-08T08:27:17.257-05:00",
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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 Robot Keeps the Controller and Relearns the View",
    "publisher": "The Machine Press",
    "published_at": "2026-11-08T09:00:00.000-05:00",
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