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arXiv · 2505.17373

Value-Guided Search for Efficient Chain-of-Thought Reasoning

Kaiwen Wang·Jin Peng Zhou·Jonathan Chang·Zhaolin Gao·Nathan Kallus·Kianté Brantley·Wen Sun
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Abstract

In this paper, we propose a simple and efficient method for value model training on long-context reasoning traces. Compared to existing process reward models (PRMs), our method does not require a fine-grained notion of "step," which is difficult to define for long-context reasoning models. By collecting a dataset of 2.5 million reasoning traces, we train a 1.5B token-level value model and apply it to DeepSeek models for improved performance with test-time compute scaling. We find that block-wise value-guided search (VGS) with a final weighted majority vote achieves better test-time scaling than standard methods such as majority voting or best-of-n. Moreover, VGS significantly reduces the inference FLOPs required to achieve the same performance of majority voting. Our dataset, model and codebase are open-sourced.

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Kaiwen Wang, Jin Peng Zhou, Jonathan Chang, Zhaolin Gao, Nathan Kallus, Kianté Brantley, Wen Sun. 2025-05-23. Value-Guided Search for Efficient Chain-of-Thought Reasoning. https://arxiv.org/abs/2505.17373

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