{
  "id": "2610.35804",
  "title": "Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models",
  "authors": "Aidar Myrzakhan, Zhiqiang Shen",
  "first_public": "2026-10-26",
  "added_at": "2026-11-02",
  "venue": "arXiv",
  "arxiv": "2610.35804",
  "paper_url": "https://arxiv.org/abs/2610.35804",
  "html_url": "https://arxiv.org/html/2610.35804v1",
  "lens": [
    "beyond-sequential"
  ],
  "representation": [
    "discrete-token"
  ],
  "computation": [
    "diffusion",
    "parallel"
  ],
  "training": [
    "distillation",
    "supervised"
  ],
  "questions": [
    "efficiency",
    "capability"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Constructs diffusion decoding schedules that reveal tokens useful for predicting the remaining positions. Distills these schedules into the model while keeping the target responses fixed, and tests faster parallel decoding on math and code tasks.",
  "provenance": "Primary-source literature review, 2026-11-02",
  "author_names": [
    "Aidar Myrzakhan",
    "Zhiqiang Shen"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.35804/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-35804",
  "citation_date": "2026-10-26",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.35804/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.35804/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.35804/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.35804/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.35804"
}