Process-based Self-Rewarding Language Models
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 self-rewarding to achieve LLM reasoning that may surpass human
capabilities.
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Shimao Zhang
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2025-03-06T19:50:45.146Z
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Shimao Zhang
user
Shimao-Zhang
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Xiao Liu
extracted_confirmed
2025-03-06T12:14:52.016Z
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Xiao Liu
user
lx865712528
67c991abe7dba9cdf9f41e69
Xin Zhang
67c991abe7dba9cdf9f41e6a
Junxiao Liu
claimed_verified
2025-05-27T07:56:21.963Z
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LiuJunxiao
user
master-lan
67c991abe7dba9cdf9f41e6b
Zheheng Luo
67c991abe7dba9cdf9f41e6c
Shujian Huang
67c991abe7dba9cdf9f41e6d
Yeyun Gong
Process-based Self-Rewarding Language Models
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 self-rewarding to achieve LLM reasoning that may surpass human
capabilities.
A Process-based Self-Rewarding pipeline enhances LLMs' mathematical reasoning by iteratively evaluating and optimizing their own outputs.
Large Language Models self-rewarding LLM-as-a-Judge step-wise preference optimization mathematical reasoning Process-based Self-Rewarding