{
  "id": 10615521,
  "title": "Verifier Errors in RLVR: Reward Hacking, Limits of Feedback, and Selective Control",
  "url": "https://urgent.news/2026/09/28/verifier-errors-in-rlvr-reward-hacking-limits-of-feedback-and",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-28T17:30:24.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.35677v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "In reinforcement learning with verifiable rewards (RLVR), imperfect verifiers can reward incorrect responses, creating opportunities for reward hacking. Using gradient flow with a fixed verifier, we characterize the conditions under which reward rises while correctness falls. We then show that the observations available during RLVR are, in general, insufficient to detect or identify accepted…",
  "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."
}