Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier

• URL: http://arxiv.org/abs/2505.11966v1
• Date: Sat, 17 May 2025 11:41:44 GMT
• Title: Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier
• Authors: Jianyuan Zhong, Zeju Li, Zhijian Xu, Xiangyu Wen, Kezhi Li, Qiang Xu,
• Abstract summary: Large Language Model (LLM) reasoning for complex tasks inherently involves a trade-off between solution accuracy and computational efficiency.<n>We introduce FlexiVe, a novel generative verifier that balances flexibly computational resources between rapid, reliable fast thinking and meticulous slow thinking.<n>Experiments show FlexiVe achieves superior accuracy in pinpointing errors within reasoning traces on ProcessBench.
• Score: 13.980380294971093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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