Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative
  Verifier
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.
682d42808560f4baf596643b
Jianyuan Zhong
claimed_verified
2025-05-21T08:40:43.900Z
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Zhong
user
Jianyuan1
682d42808560f4baf596643c
Zeju Li
claimed_verified
2025-05-22T07:17:49.417Z
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Zeju Li
user
zeju-0727
682d42808560f4baf596643d
Zhijian Xu
claimed_verified
2025-06-18T12:17:48.425Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/hCmEo__IWO_R_Ps8Q52Os.png
zhijianxu
user
VEWOXIC
682d42808560f4baf596643e
Xiangyu Wen
admin_assigned
2025-05-21T10:24:05.361Z
641b1b36a5f876fe30c49542
/avatars/ac9267925f45d325c2adb2eb0e38077b.svg
Xiangyu Wen
user
XiangyuWen
682d42808560f4baf596643f
Kezhi Li
682d42808560f4baf5966440
Qiang Xu
Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative
  Verifier
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.
FlexiVe, a novel generative verifier, optimally balances computational resources for enhanced LLM reasoning, improving accuracy and efficiency on complex tasks.
Large Language Model (LLM) verification Generative Reward Models (GenRMs) Flexible Allocation of Verification Budget Solve-Detect-Verify pipeline ProcessBench mathematical reasoning benchmarks self-consistency