Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative
  Verifier
Authors: name: Jianyuan Zhong, name: Zeju Li, name: Zhijian Xu, name: Xiangyu Wen, name: Kezhi Li, name: Qiang Xu

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.