{
  "id": 16704990,
  "title": "Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models",
  "url": "https://urgent.news/2026/10/26/learning-when-to-commit-schedule-distillation-for-fast-diffusion",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-26T17:35:58.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.35804v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Diffusion large language models (dLLMs) can generate multiple tokens in parallel, but often require many decoding rounds to complete a response. Efficient generation depends not only on which tokens are easy to predict, but also on how committing to them helps predict the remaining positions. A highly predictable token may provide little useful context, while revealing a less predictable token…",
  "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."
}