{
  "id": 16002019,
  "title": "Asymmetric On-Policy Distillation Learns In-Context Exploration",
  "url": "https://urgent.news/2026/10/21/asymmetric-on-policy-distillation-learns-in-context-exploration",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-21T17:50:31.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.30105v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Sequential decision-making under partial observability is difficult because agents must explore to infer hidden, task-relevant information before acting effectively. Learning such information-gathering behaviors tabula rasa via online reinforcement learning is sample-inefficient, whereas offline imitation averages expert actions across hidden states and fails to learn exploration. In this work,…",
  "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."
}