@inproceedings{zhong-etal-2025-dyve,
title = "Dyve: Thinking Fast and Slow for Dynamic Process Verification",
author = "Zhong, Jianyuan and
Li, Zeju and
Xu, Zhijian and
Wen, Xiangyu and
Xu, Qiang",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1136/",
pages = "22331--22344",
ISBN = "979-8-89176-332-6",
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."
}Markdown (Informal)
[Dyve: Thinking Fast and Slow for Dynamic Process Verification](https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1136/) (Zhong et al., EMNLP 2025)
ACL