Self-rewarding correction for mathematical reasoning

• URL: http://arxiv.org/abs/2502.19613v1
• Date: Wed, 26 Feb 2025 23:01:16 GMT
• Title: Self-rewarding correction for mathematical reasoning
• Authors: Wei Xiong, Hanning Zhang, Chenlu Ye, Lichang Chen, Nan Jiang, Tong Zhang,
• Abstract summary: We study self-rewarding reasoning large language models (LLMs)<n>LLMs can simultaneously generate step-by-step reasoning and evaluate the correctness of their outputs during the inference time-without external feedback.<n>We propose a two-staged algorithmic framework for constructing self-rewarding reasoning models using only self-generated data.
• Score: 19.480508580498103
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We study self-rewarding reasoning large language models (LLMs), which can simultaneously generate step-by-step reasoning and evaluate the correctness of their outputs during the inference time-without external feedback. This integrated approach allows a single model to independently guide its reasoning process, offering computational advantages for model deployment. We particularly focus on the representative task of self-correction, where models autonomously detect errors in their responses, revise outputs, and decide when to terminate iterative refinement loops. To enable this, we propose a two-staged algorithmic framework for constructing self-rewarding reasoning models using only self-generated data. In the first stage, we employ sequential rejection sampling to synthesize long chain-of-thought trajectories that incorporate both self-rewarding and self-correction mechanisms. Fine-tuning models on these curated data allows them to learn the patterns of self-rewarding and self-correction. In the second stage, we further enhance the models' ability to assess response accuracy and refine outputs through reinforcement learning with rule-based signals. Experiments with Llama-3 and Qwen-2.5 demonstrate that our approach surpasses intrinsic self-correction capabilities and achieves performance comparable to systems that rely on external reward models.

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