{
  "id": 17905449,
  "title": "Satisfied, Not Solving: Wireheading of Bounded Homeostatic Rewards in Reinforcement Learning",
  "url": "https://urgent.news/2026/10/31/satisfied-not-solving-wireheading-of-bounded-homeostatic-rewards-in",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-31T18:30:01.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.01183v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Bounded intrinsic rewards are often assumed to be safe from exploitation: if a signal can yield at most a fixed amount, gaming it should not pay. We test this for a homeostatic satiation reward, in which needs build up and relieving them is rewarding, and give the agent a cheap action that resets its needs. In MiniGrid DoorKey-8x8, PPO agents with this action solved 2 of 80 seeds, against 71 of…",
  "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."
}