title: Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative
    Verifier
summary: Large Language Model (LLM) reasoning for complex tasks inherently involves a
  trade-off between solution accuracy and computational efficiency. The
  subsequent step of verification, while intended to improve performance, further
  complicates this landscape by introducing its own challenging trade-off:
  sophisticated Generative Reward Models (GenRMs) can be computationally
  prohibitive if naively integrated with LLMs at test-time, while simpler, faster
  methods may lack reliability. To overcome these challenges, we introduce
  FlexiVe, a novel generative verifier that flexibly balances computational
  resources between rapid, reliable fast thinking and meticulous slow thinking
  using a Flexible Allocation of Verification Budget strategy. We further propose
  the Solve-Detect-Verify pipeline, an efficient inference-time scaling framework
  that intelligently integrates FlexiVe, proactively identifying solution
  completion points to trigger targeted verification and provide focused solver
  feedback. Experiments show FlexiVe achieves superior accuracy in pinpointing
  errors within reasoning traces on ProcessBench. Furthermore, on challenging
  mathematical reasoning benchmarks (AIME 2024, AIME 2025, and CNMO), our full
  approach outperforms baselines like self-consistency in reasoning accuracy and
  inference efficiency. Our system offers a scalable and effective solution to
  enhance LLM reasoning at test time.
paper_authors._id: 682d42808560f4baf596643b
paper_authors.name: Jianyuan Zhong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T08:40:43.900Z
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: 682d42808560f4baf596643c
paper_authors.name: Zeju Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-22T07:17:49.417Z
paper_authors._id: 664ac4f7fe822b08e6f06814
paper_authors.avatarUrl: /avatars/23193494fcc8e58faf1eee5f1223aca6.svg
paper_authors.fullname: Zeju Li
paper_authors.type: user
paper_authors.user: zeju-0727
paper_authors._id: 682d42808560f4baf596643d
paper_authors.name: Zhijian Xu
paper_authors._id: 682d42808560f4baf596643e
paper_authors.name: Xiangyu Wen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-21T10:24:05.361Z
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: 682d42808560f4baf596643f
paper_authors.name: Kezhi Li
paper_authors._id: 682d42808560f4baf5966440
paper_authors.name: Qiang Xu
paper_title: Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative
    Verifier
paper_summary: Large Language Model (LLM) reasoning for complex tasks inherently involves a
  trade-off between solution accuracy and computational efficiency. The
  subsequent step of verification, while intended to improve performance, further
  complicates this landscape by introducing its own challenging trade-off:
  sophisticated Generative Reward Models (GenRMs) can be computationally
  prohibitive if naively integrated with LLMs at test-time, while simpler, faster
  methods may lack reliability. To overcome these challenges, we introduce
  FlexiVe, a novel generative verifier that flexibly balances computational
  resources between rapid, reliable fast thinking and meticulous slow thinking
  using a Flexible Allocation of Verification Budget strategy. We further propose
  the Solve-Detect-Verify pipeline, an efficient inference-time scaling framework
  that intelligently integrates FlexiVe, proactively identifying solution
  completion points to trigger targeted verification and provide focused solver
  feedback. Experiments show FlexiVe achieves superior accuracy in pinpointing
  errors within reasoning traces on ProcessBench. Furthermore, on challenging
  mathematical reasoning benchmarks (AIME 2024, AIME 2025, and CNMO), our full
  approach outperforms baselines like self-consistency in reasoning accuracy and
  inference efficiency. Our system offers a scalable and effective solution to
  enhance LLM reasoning at test time.
paper_ai_summary: FlexiVe, a novel generative verifier, optimally balances computational resources for enhanced LLM reasoning, improving accuracy and efficiency on complex tasks.
paper_ai_keywords: Large Language Model (LLM) verification Generative Reward Models (GenRMs) Flexible Allocation of Verification Budget Solve-Detect-Verify pipeline ProcessBench mathematical reasoning benchmarks self-consistency