{
  "id": 246287,
  "title": "EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning",
  "url": "https://urgent.news/2026/08/06/envace-internalizing-environment-dynamics-via-world-rehearsal-for",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-06T15:54:36.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.06197v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Training large language model agents for long-horizon tool use typically relies on interactions with real or synthesized executable environments, whose construction and verification are costly, or on external simulators that are difficult to ground. We introduce EnvACE, an agentic reinforcement learning method that replaces external environment interaction during training with world rehearsal.…",
  "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."
}