title: Dyve: Thinking Fast and Slow for Dynamic Process Verification
summary: We present Dyve, a dynamic process verifier that enhances reasoning error
  detection in large language models by integrating fast and slow thinking,
  inspired by Kahneman's Systems Theory. Dyve adaptively applies immediate
  token-level confirmation System 1 for straightforward steps and comprehensive
  analysis System 2 for complex ones. Leveraging a novel step-wise
  consensus-filtered process supervision technique, combining Monte Carlo
  estimation with LLM based evaluation, Dyve curates high-quality supervision
  signals from noisy data. Experimental results on ProcessBench and the MATH
  dataset confirm that Dyve significantly outperforms existing process-based
  verifiers and boosts performance in Best-of-N settings.
paper_authors._id: 67b44baa5fd91177ed7760a2
paper_authors.name: Jianyuan Zhong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:30:45.385Z
paper_authors._id: 6608fa4f5baec84322ec85ea
paper_authors.avatarUrl: /avatars/13bdaff931676b065fa1efef06fef922.svg
paper_authors.fullname: Zhong
paper_authors.type: user
paper_authors.user: Jianyuan1
paper_authors._id: 67b44baa5fd91177ed7760a3
paper_authors.name: Zeju Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T16:39:43.674Z
paper_authors._id: 664ac4f7fe822b08e6f06814
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paper_authors.fullname: Zeju Li
paper_authors.type: user
paper_authors.user: zeju-0727
paper_authors._id: 67b44baa5fd91177ed7760a4
paper_authors.name: Zhijian Xu
paper_authors._id: 67b44baa5fd91177ed7760a5
paper_authors.name: Xiangyu Wen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T16:39:45.646Z
paper_authors._id: 641b1b36a5f876fe30c49542
paper_authors.avatarUrl: /avatars/ac9267925f45d325c2adb2eb0e38077b.svg
paper_authors.fullname: Xiangyu Wen
paper_authors.type: user
paper_authors.user: XiangyuWen
paper_authors._id: 67b44baa5fd91177ed7760a6
paper_authors.name: Qiang Xu
paper_title: Dyve: Thinking Fast and Slow for Dynamic Process Verification
paper_summary: We present Dyve, a dynamic process verifier that enhances reasoning error
  detection in large language models by integrating fast and slow thinking,
  inspired by Kahneman's Systems Theory. Dyve adaptively applies immediate
  token-level confirmation System 1 for straightforward steps and comprehensive
  analysis System 2 for complex ones. Leveraging a novel step-wise
  consensus-filtered process supervision technique, combining Monte Carlo
  estimation with LLM based evaluation, Dyve curates high-quality supervision
  signals from noisy data. Experimental results on ProcessBench and the MATH
  dataset confirm that Dyve significantly outperforms existing process-based
  verifiers and boosts performance in Best-of-N settings.
paper_ai_keywords: Monte Carlo estimation step-wise consensus-filtered process supervision