{
  "id": "2610.35201",
  "title": "DTCA: Denoising Token Credit Assignment for Reinforcement Learning in Diffusion Language Models",
  "authors": "Hongze Chen, Miao Pan, Lizhou Liang, Qinfeng Li, Xinyin Ma, Yongliang Shen, Weishi Wang, Daniel Dahlmeier, Xuhong Zhang, Wenqi Zhang",
  "first_public": "2026-10-26",
  "added_at": "2026-11-02",
  "venue": "arXiv",
  "arxiv": "2610.35201",
  "paper_url": "https://arxiv.org/abs/2610.35201",
  "html_url": "https://arxiv.org/html/2610.35201v1",
  "lens": [
    "beyond-sequential"
  ],
  "representation": [
    "discrete-token"
  ],
  "computation": [
    "diffusion",
    "parallel"
  ],
  "training": [
    "reinforcement-learning"
  ],
  "questions": [
    "capability",
    "efficiency"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Estimates a token’s contribution by how much revealing it improves predictions at other masked positions. Uses part-of-speech groups to select denoising states and assign reinforcement-learning credit, improving a diffusion language model on math problems.",
  "provenance": "Primary-source literature review, 2026-11-02",
  "author_names": [
    "Hongze Chen",
    "Miao Pan",
    "Lizhou Liang",
    "Qinfeng Li",
    "Xinyin Ma",
    "Yongliang Shen",
    "Weishi Wang",
    "Daniel Dahlmeier",
    "Xuhong Zhang",
    "Wenqi Zhang"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.35201/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-35201",
  "citation_date": "2026-10-26",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.35201/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.35201/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.35201/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.35201/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.35201"
}