{
  "id": 11083813,
  "title": "PhantomEnvironments: Training LLM Agents in Fictional Worlds",
  "url": "https://urgent.news/2026/09/30/phantomenvironments-training-llm-agents-in-fictional-worlds",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-30T17:26:57.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.40221v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Training LLM agents with reinforcement learning (RL) is bottlenecked by environments, which must provide verifiable rewards, support long-horizon interaction, and scale cheaply. Existing approaches rely on costly human-curated data or on LLM-generated environments that risk hallucinations and benchmark contamination. We show that LLMs can instead be trained into capable search agents using…",
  "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."
}