{
  "id": 5001693,
  "title": "Selective Agent Guidance via Entropy: Learning Autonomous Policies from Imperfect VLM Teachers",
  "url": "https://urgent.news/2026/09/01/selective-agent-guidance-via-entropy-learning-autonomous-policies",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-01T17:33:41.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.01567v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Vision-Language Models (VLMs) provide useful priors for interactive decision-making, but using them directly as policies is expensive and brittle: they must be queried at every step, do not improve from environment interaction, and can repeat systematic errors. We study how to learn a cheap autonomous policy from an online, expensive, and imperfect but informative VLM teacher. We propose SAGE…",
  "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."
}