{
  "id": 4777360,
  "title": "Reconciling Process Supervision with Outcome-Based Credit in Agentic Policy Optimization",
  "url": "https://urgent.news/2026/08/31/reconciling-process-supervision-with-outcome-based-credit-in-agentic",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-31T16:51:50.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.31077v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Outcome-based reinforcement learning provides verified feedback for language-model agents, but assigns trajectory-level advantage uniformly to all decisions, yielding coarse credit over long-horizon interactions. On-policy self-distillation offers finer supervision by re-evaluating sampled behavior with privileged information (PI) available only during training. However, fine-grained supervision…",
  "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."
}