{
  "id": "2610.19408",
  "title": "ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models",
  "authors": "Zhaoyan Jia, Chengwei Zhou, Gourav Datta, Massoud Pedram",
  "first_public": "2026-10-14",
  "lens": [
    "beyond-sequential"
  ],
  "representation": [
    "discrete-token"
  ],
  "training": [
    "reinforcement-learning"
  ],
  "questions": [
    "capability",
    "efficiency"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Branches rollouts from uncertain intermediate states and reuses their shared textual prefixes. Local outcome comparisons assign credit to reasoning segments, testing whether more targeted exploration improves diffusion-model reasoning with fewer generated training tokens.",
  "computation": [
    "diffusion",
    "search"
  ],
  "venue": "arXiv",
  "arxiv": "2610.19408",
  "paper_url": "https://arxiv.org/abs/2610.19408",
  "provenance": "Primary-source literature search, 2026-10-18",
  "added_at": "2026-10-18",
  "author_names": [
    "Zhaoyan Jia",
    "Chengwei Zhou",
    "Gourav Datta",
    "Massoud Pedram"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.19408/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-19408",
  "citation_date": "2026-10-14",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.19408/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.19408/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.19408/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.19408/citation.csl.json"
}