Authors: Shimao Zhang, Xiao Liu, Xin Zhang, Junxiao Liu, Zheheng Luo, Shujian Huang, Yeyun Gong
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 self-rewarding to achieve LLM reasoning that may surpass human
capabilities.
Source: http://arxiv.org/abs/2503.03746v1
