{
  "id": 18022,
  "url": "https://arxiv.org/abs/2608.09226v1",
  "title": "RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation",
  "summary": "Efficient text-to-image generation requires both reinforcement-learning (RL)-based reward alignment and few-step distillation, yet these procedures are typically performed sequentially, increasing training cost and risking the loss of reward gains during compression. We instead take an RL-native perspective: diffusion RL already generates reward-scored finite-step trajectories, whose intermediate states provide a natural source of distillation supervision rather than a disposable byproduct of sa",
  "authors": "Yuhan Li, Fangao Zeng, Sicong Kang, Mengfei Xu, Hao Zhou, Wei Li et al.",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T07:49:05.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18022",
  "original_url": "https://arxiv.org/abs/2608.09226v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}