{
  "id": 4572,
  "url": "https://arxiv.org/abs/2605.10236v3",
  "title": "When Does Non-Uniform Replay Matter in Reinforcement Learning?",
  "summary": "Modern off-policy reinforcement learning algorithms often rely on simple uniform replay sampling and it remains unclear when and why non-uniform replay improves over this strong baseline. Across diverse RL settings, we show that the effectiveness of non-uniform replay is governed by three factors: replay volume, the number of replayed transitions per environment step; expected recency, how recent sampled transitions are; and the entropy of the replay sampling distribution. Our main contribution ",
  "authors": "Michal Korniak, Mikołaj Czarnecki, Yarden As, Piotr Miłoś, Pieter Abbeel, Michal Nauman",
  "category": "research",
  "topics": "regulation,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-11T09:11:05.000Z",
  "fetched_at": "2026-07-14T16:31:08.353Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4572",
  "original_url": "https://arxiv.org/abs/2605.10236v3",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}