{
  "id": 16002022,
  "title": "AtomicRL: Atomic Reinforcement Learning for Long-Horizon Robotic Manipulation",
  "url": "https://urgent.news/2026/10/21/atomicrl-atomic-reinforcement-learning-for-long-horizon-robotic",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-21T17:45:24.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.30097v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Vision-language-action (VLA) models have shown promise in robotic manipulation, but imitation learning (IL) alone often struggles to master complex long-horizon skills. Reinforcement learning (RL) offers an effective avenue for further improving VLA policies. However, existing VLA-RL methods typically suffer from delayed and biased credit assignment due to trajectory-level sparse rewards, while…",
  "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."
}