@inproceedings{chan-etal-2025-boosting,
title = "Boosting Policy and Process Reward Models with {M}onte {C}arlo Tree Search in Open-Domain {QA}",
author = "Chan, Chi-Min and
Xu, Chunpu and
Zhu, Junqi and
Ji, Jiaming and
Hong, Donghai and
Wen, Pengcheng and
Jiang, Chunyang and
Ye, Zhen and
Yang, Yaodong and
Xue, Wei and
Han, Sirui and
Guo, Yike",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.388/",
doi = "10.18653/v1/2025.findings-acl.388",
pages = "7433--7451",
ISBN = "979-8-89176-256-5",
abstract = "The recent introduction of OpenAI{'}s O1/O3 model represents a significant milestone in developing strong reasoning capabilities in Large Language Models (LLMs). By introducing more computational budget during test-time, LLMs have the potential to explore more accurate and higher-quality solutions. However, such paradigms are primarily verified in domains that have well-defined criteria for responses, such as coding and mathematics. Inspired by the success of this paradigm, we aim to bridge it to more subtle open-domain question answering. Specifically, we utilize search mechanisms such as Monte Carlo Tree Search (MCTS) for both policy model improvement and reward model improvement that achieve better performance in test-time scaling strategies. Our contributions are summarized in two folds: For the training phase, we demonstrate that our approach surpasses previous SOTA automatic data annotation methods and various public instruction-tuning datasets, with fewer data points. This offers a more data-efficient solution for training robust models. For the inference phase, we utilize the intermediate values collected during training data construction to train a process reward model called PRM+. This model employs a novel two-stage training method to provide finer-grained guidance across the generation trajectory. This introduces no additional overhead during training data collection and further enhances performance by scaling test-time computation. Experimental results show that our method can effectively improve the performance of both the policy model and the reward model."
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<abstract>The recent introduction of OpenAI’s O1/O3 model represents a significant milestone in developing strong reasoning capabilities in Large Language Models (LLMs). By introducing more computational budget during test-time, LLMs have the potential to explore more accurate and higher-quality solutions. However, such paradigms are primarily verified in domains that have well-defined criteria for responses, such as coding and mathematics. Inspired by the success of this paradigm, we aim to bridge it to more subtle open-domain question answering. Specifically, we utilize search mechanisms such as Monte Carlo Tree Search (MCTS) for both policy model improvement and reward model improvement that achieve better performance in test-time scaling strategies. Our contributions are summarized in two folds: For the training phase, we demonstrate that our approach surpasses previous SOTA automatic data annotation methods and various public instruction-tuning datasets, with fewer data points. This offers a more data-efficient solution for training robust models. For the inference phase, we utilize the intermediate values collected during training data construction to train a process reward model called PRM+. This model employs a novel two-stage training method to provide finer-grained guidance across the generation trajectory. This introduces no additional overhead during training data collection and further enhances performance by scaling test-time computation. Experimental results show that our method can effectively improve the performance of both the policy model and the reward model.</abstract>
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%0 Conference Proceedings
%T Boosting Policy and Process Reward Models with Monte Carlo Tree Search in Open-Domain QA
%A Chan, Chi-Min
%A Xu, Chunpu
%A Zhu, Junqi
%A Ji, Jiaming
%A Hong, Donghai
%A Wen, Pengcheng
%A Jiang, Chunyang
%A Ye, Zhen
%A Yang, Yaodong
%A Xue, Wei
%A Han, Sirui
%A Guo, Yike
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F chan-etal-2025-boosting
%X The recent introduction of OpenAI’s O1/O3 model represents a significant milestone in developing strong reasoning capabilities in Large Language Models (LLMs). By introducing more computational budget during test-time, LLMs have the potential to explore more accurate and higher-quality solutions. However, such paradigms are primarily verified in domains that have well-defined criteria for responses, such as coding and mathematics. Inspired by the success of this paradigm, we aim to bridge it to more subtle open-domain question answering. Specifically, we utilize search mechanisms such as Monte Carlo Tree Search (MCTS) for both policy model improvement and reward model improvement that achieve better performance in test-time scaling strategies. Our contributions are summarized in two folds: For the training phase, we demonstrate that our approach surpasses previous SOTA automatic data annotation methods and various public instruction-tuning datasets, with fewer data points. This offers a more data-efficient solution for training robust models. For the inference phase, we utilize the intermediate values collected during training data construction to train a process reward model called PRM+. This model employs a novel two-stage training method to provide finer-grained guidance across the generation trajectory. This introduces no additional overhead during training data collection and further enhances performance by scaling test-time computation. Experimental results show that our method can effectively improve the performance of both the policy model and the reward model.
%R 10.18653/v1/2025.findings-acl.388
%U https://aclanthology.org/2025.findings-acl.388/
%U https://doi.org/10.18653/v1/2025.findings-acl.388
%P 7433-7451
Markdown (Informal)
[Boosting Policy and Process Reward Models with Monte Carlo Tree Search in Open-Domain QA](https://aclanthology.org/2025.findings-acl.388/) (Chan et al., Findings 2025)
ACL
- Chi-Min Chan, Chunpu Xu, Junqi Zhu, Jiaming Ji, Donghai Hong, Pengcheng Wen, Chunyang Jiang, Zhen Ye, Yaodong Yang, Wei Xue, Sirui Han, and Yike Guo. 2025. Boosting Policy and Process Reward Models with Monte Carlo Tree Search in Open-Domain QA. In Findings of the Association for Computational Linguistics: ACL 2025, pages 7433–7451, Vienna, Austria. Association for Computational Linguistics.