Value-Guided Search for Efficient Chain-of-Thought Reasoning
Kaiwen Wang, Jin Zhou, Jonathan Chang, Zhaolin Gao, Nathan Kallus, Kianté Brantley, Wen Sun
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-$n$. With an inference budget of 64 generations, VGS with \texttt{DeepSeek-R1-Distill-1.5B} achieves an average accuracy of $45.7\%$ across four competition math benchmarks (AIME 2024 \& 2025, HMMT Feb 2024 \& 2025), reaching parity with o3-mini-medium.Moreover, VGS significantly reduces the inference FLOPs required to achieve the same performance of majority voting. Our dataset, model and codebase are open-sourced at ANONYMIZED.