{
  "id": 10602332,
  "title": "UOPD: Uncertainty-Aware Intervention for On-Policy Distillation of Multi-Turn Agents",
  "url": "https://urgent.news/2026/09/27/uopd-uncertainty-aware-intervention-for-on-policy-distillation-of",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-27T23:57:28.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.34036v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "On-policy distillation (OPD) trains a student on its own rollouts using dense supervision from a teacher. In multi-turn environments, a mistake at a critical decision step can redirect the subsequent rollout toward poor outcomes. We use low teacher confidence on student actions to select high-uncertainty steps for correction. In a controlled ALFWorld study, a single teacher correction at 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."
}