Dyve: Thinking Fast and Slow for Dynamic Process Verification

Jianyuan Zhong, Zeju Li, Zhijian Xu, Xiangyu Wen, Qiang Xu


Abstract
Large Language Models have advanced significantly in complex reasoning, often leveraging external reward model to improve the reliability of their multi-step processes. However, existing process verification methods struggle with reliably assessing incomplete reasoning traces and are limited by the cost of high-quality human annotations or the inherent noise in automatically generated labels. Therefore, 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. Unlike traditional verifiers that only evaluate final outputs, Dyve employs a step-wise consensus-filtered supervision strategy, leveraging Monte Carlo estimation, LLM-as-a-Judge, and specialized reasoning models to extract high-quality training signals from noisy rollouts. 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 while maintaining computational efficiency by strategically allocating verification resources.
Anthology ID:
2025.emnlp-main.1136
Volume:
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, Violet Peng
Venue:
EMNLP
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Publisher:
Association for Computational Linguistics
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Pages:
22331–22344
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URL:
https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1136/
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Cite (ACL):
Jianyuan Zhong, Zeju Li, Zhijian Xu, Xiangyu Wen, and Qiang Xu. 2025. Dyve: Thinking Fast and Slow for Dynamic Process Verification. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 22331–22344, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Dyve: Thinking Fast and Slow for Dynamic Process Verification (Zhong et al., EMNLP 2025)
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