{
  "id": 4544331,
  "title": "Program Learning with Verifiable Rewards: Symbolic Backpropagation for Post-Training LLMs",
  "url": "https://urgent.news/2026/08/28/program-learning-with-verifiable-rewards-symbolic-backpropagation-for",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-28T15:06:00.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.28421v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Post training a language model to reason means updating its weights. Supervised finetuning and reinforcement learning both place the acquired capability inside the model where it cannot be inspected cannot be checked step by step and cannot be moved to another model. We argue that for tasks whose intermediate steps admit verification, reasoning is better placed outside the base models weights as…",
  "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."
}