{
  "id": 14601094,
  "title": "Co-Evolving Acting and Forecasting for Language Agents",
  "url": "https://urgent.news/2026/10/14/co-evolving-acting-and-forecasting-for-language-agents",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-14T17:59:28.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.20597v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Training language agents is bottlenecked by the high cost of long-horizon rollouts, yet standard agentic reinforcement learning (RL) learns only from a terminal scalar reward, leaving the supervision in intermediate steps largely unused. Prior work exploits this supervision through value targets that estimate per-step success probabilities or observation targets that fit environment response.…",
  "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."
}