@inproceedings{zhang-etal-2025-process,
title = "Process-based Self-Rewarding Language Models",
author = "Zhang, Shimao and
Liu, Xiao and
Zhang, Xin and
Liu, Junxiao and
Luo, Zheheng and
Huang, Shujian and
Gong, Yeyun",
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.930/",
doi = "10.18653/v1/2025.findings-acl.930",
pages = "18097--18110",
ISBN = "979-8-89176-256-5",
abstract = "Large Language Models have demonstrated outstanding performance across various downstream tasks and have been widely applied in multiple scenarios. Human-annotated preference data is used for training to further improve LLMs' performance, which is constrained by the upper limit of human performance. Therefore, Self-Rewarding method has been proposed, where LLMs generate training data by rewarding their own outputs. However, the existing self-rewarding paradigm is not effective in mathematical reasoning scenarios and may even lead to a decline in performance. In this work, we propose the Process-based Self-Rewarding pipeline for language models, which introduces long-thought reasoning, step-wise LLM-as-a-Judge, and step-wise preference optimization within the self-rewarding paradigm. Our new paradigm successfully enhances the performance of LLMs on multiple mathematical reasoning benchmarks through iterative Process-based Self-Rewarding, demonstrating the immense potential of process-based self-rewarding to achieve LLM reasoning that may surpass human capabilities."
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<abstract>Large Language Models have demonstrated outstanding performance across various downstream tasks and have been widely applied in multiple scenarios. Human-annotated preference data is used for training to further improve LLMs’ performance, which is constrained by the upper limit of human performance. Therefore, Self-Rewarding method has been proposed, where LLMs generate training data by rewarding their own outputs. However, the existing self-rewarding paradigm is not effective in mathematical reasoning scenarios and may even lead to a decline in performance. In this work, we propose the Process-based Self-Rewarding pipeline for language models, which introduces long-thought reasoning, step-wise LLM-as-a-Judge, and step-wise preference optimization within the self-rewarding paradigm. Our new paradigm successfully enhances the performance of LLMs on multiple mathematical reasoning benchmarks through iterative Process-based Self-Rewarding, demonstrating the immense potential of process-based self-rewarding to achieve LLM reasoning that may surpass human capabilities.</abstract>
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%0 Conference Proceedings
%T Process-based Self-Rewarding Language Models
%A Zhang, Shimao
%A Liu, Xiao
%A Zhang, Xin
%A Liu, Junxiao
%A Luo, Zheheng
%A Huang, Shujian
%A Gong, Yeyun
%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 zhang-etal-2025-process
%X Large Language Models have demonstrated outstanding performance across various downstream tasks and have been widely applied in multiple scenarios. Human-annotated preference data is used for training to further improve LLMs’ performance, which is constrained by the upper limit of human performance. Therefore, Self-Rewarding method has been proposed, where LLMs generate training data by rewarding their own outputs. However, the existing self-rewarding paradigm is not effective in mathematical reasoning scenarios and may even lead to a decline in performance. In this work, we propose the Process-based Self-Rewarding pipeline for language models, which introduces long-thought reasoning, step-wise LLM-as-a-Judge, and step-wise preference optimization within the self-rewarding paradigm. Our new paradigm successfully enhances the performance of LLMs on multiple mathematical reasoning benchmarks through iterative Process-based Self-Rewarding, demonstrating the immense potential of process-based self-rewarding to achieve LLM reasoning that may surpass human capabilities.
%R 10.18653/v1/2025.findings-acl.930
%U https://aclanthology.org/2025.findings-acl.930/
%U https://doi.org/10.18653/v1/2025.findings-acl.930
%P 18097-18110
Markdown (Informal)
[Process-based Self-Rewarding Language Models](https://aclanthology.org/2025.findings-acl.930/) (Zhang et al., Findings 2025)
ACL
- Shimao Zhang, Xiao Liu, Xin Zhang, Junxiao Liu, Zheheng Luo, Shujian Huang, and Yeyun Gong. 2025. Process-based Self-Rewarding Language Models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 18097–18110, Vienna, Austria. Association for Computational Linguistics.