Value-Guided Search for Efficient Chain-of-Thought Reasoning
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 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.
6834ab9e6b152a062eb05453
Kaiwen Wang
6834ab9e6b152a062eb05454
Jin Peng Zhou
claimed_verified
2025-07-28T08:47:44.200Z
643ef7876d5be535d28084ed
/avatars/489c764bcb89c7538b83beb7f339b1b4.svg
Jin Zhou
user
jpzhou01
6834ab9e6b152a062eb05455
Jonathan Chang
6834ab9e6b152a062eb05456
Zhaolin Gao
claimed_verified
2025-05-30T06:55:06.339Z
652eec0aabc673c4204c459e
https://cdn-avatars.huggingface.co/v1/production/uploads/652eec0aabc673c4204c459e/9otSQFP8G3S8zarR1Y5rE.jpeg
Zhaolin Gao
user
GitBag
6834ab9e6b152a062eb05457
Nathan Kallus
6834ab9e6b152a062eb05458
Kianté Brantley
6834ab9e6b152a062eb05459
Wen Sun
Value-Guided Search for Efficient Chain-of-Thought Reasoning
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 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.
A simple and efficient method for value model training on long-context reasoning traces improves test-time performance and reduces computational cost compared to existing methods.
value model training long-context reasoning process reward models token-level value model block-wise value-guided search VGS inference FLOPs