{
  "id": 1619432,
  "title": "Policy Iteration with Human Feedback: Bringing Post-Training RL to In-context Learning",
  "url": "https://urgent.news/2026/08/17/policy-iteration-with-human-feedback-bringing-post-training-rl-to-in",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-17T17:16:43.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.16831v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Generative pretraining established reusable task representations; later work on language-based task conditioning and in-context learning showed that a fixed model could adapt its behavior from instructions and demonstrations. Policy Iteration with Human Feedback (PIHF) builds on this development and the recurrent evaluate-and-improve structure of generalized policy iteration. PIHF uses a…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}