{
  "title": "Update labels, train again, deploy again",
  "caption": "RECAP deployment expands data with robot rollouts and corrections. Updated value estimates give new advantage labels for the data. RECAP trains the advantage-conditioned policy again and deploys it again. Offline pretraining and task-specific SFT occur before this loop. The author made this explanatory diagram. The playback is an example, and animation time is not measured runtime.",
  "alt": "RECAP loop: collect robot rollouts and corrections, update advantage labels, retrain the policy and redeploy it after pretraining and task SFT.",
  "sources": [
    {
      "path": "content/blog/2026-05-25_from-pi0-to-pi07-open-pi-series/post.md",
      "description": "Article mechanism and retained citations"
    },
    "https://www.pi.website/blog/pistar06"
  ],
  "limitations": [
    "The playback is an example. Playback time is not measured runtime.",
    "Advantage labels depend on the quality of value estimation and relabeling.",
    "This illustrative round is not proof of a measured performance improvement."
  ]
}