{
  "id": 9068359,
  "title": "Conduit: An Experience Data Plane for Distributed Reinforcement Learning",
  "url": "https://urgent.news/2026/09/21/conduit-an-experience-data-plane-for-distributed-reinforcement",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-21T12:02:41.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.24456v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Distributed reinforcement learning (RL) scales training by parallelizing actors and learners around an Experience Buffer. As RL workloads grow, however, the buffer becomes more than a replay queue: it is the storage substrate of a large-capacity, latency-critical experience path that every iteration traverses to move, transform, sample, and batch experiences before learner updates can begin.…",
  "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."
}