{
  "id": 16828115,
  "title": "Vision-Language Agents Can Learn in Their Own Latent Worlds",
  "url": "https://urgent.news/2026/10/27/vision-language-agents-can-learn-in-their-own-latent-worlds",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-27T09:51:08.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.36925v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Learning from experience is essential for building the next generation of agents that continually expand their capabilities. Pretrained vision-language models (VLMs) already internalize rich world knowledge, yet this knowledge is rarely leveraged to drive policy improvement through online interaction. Realizing this potential efficiently remains challenging, as policy updates shift the agent's…",
  "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."
}