{
  "id": "2610.00661",
  "title": "Exploring More, Reasoning Better: Stepwise Risk-Sensitive GRPO for Diffusion Language Models",
  "authors": "Yue Yu, Bowen Zuo, David Crandall, Yinglun Zhu, Dongruo Zhou",
  "first_public": "2026-09-30",
  "lens": [
    "beyond-sequential"
  ],
  "representation": [
    "discrete-token"
  ],
  "training": [
    "reinforcement-learning"
  ],
  "questions": [
    "capability",
    "scaling"
  ],
  "evidence": [
    "empirical",
    "formal"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Changes how outcome rewards weight decisions at different denoising states during diffusion-model reinforcement learning. Derives the effect for binary rewards and uses matched-scale controls to test whether the schedule improves mathematical accuracy and reasoning-trace diversity.",
  "computation": [
    "diffusion",
    "parallel"
  ],
  "venue": "arXiv",
  "arxiv": "2610.00661",
  "paper_url": "https://arxiv.org/abs/2610.00661",
  "provenance": "Primary-source literature search, 2026-10-18",
  "added_at": "2026-10-18",
  "author_names": [
    "Yue Yu",
    "Bowen Zuo",
    "David Crandall",
    "Yinglun Zhu",
    "Dongruo Zhou"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.00661/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-00661",
  "citation_date": "2026-09-30",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.00661/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.00661/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.00661/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.00661/citation.csl.json"
}