Self-Training Large Language Models with Confident Reasoning

TL;DR

This paper introduces CORE-PO, a self-training approach that enhances large language models by focusing on high-confidence reasoning paths, leading to improved accuracy across multiple benchmarks.

Contribution

It proposes a novel confidence-based self-training method that emphasizes reasoning-level confidence, outperforming existing approaches in LLM fine-tuning.

Findings

Improves accuracy on in-distribution benchmarks

Enhances out-of-distribution generalization

Focuses on reasoning path quality for better training

Abstract

Large language models (LLMs) have shown impressive performance by generating reasoning paths before final answers, but learning such a reasoning path requires costly human supervision. To address this issue, recent studies have explored self-training methods that improve reasoning capabilities using pseudo-labels generated by the LLMs themselves. Among these, confidence-based self-training fine-tunes LLMs to prefer reasoning paths with high-confidence answers, where confidence is estimated via majority voting. However, such methods exclusively focus on the quality of the final answer and may ignore the quality of the reasoning paths, as even an incorrect reasoning path leads to a correct answer by chance. Instead, we advocate the use of reasoning-level confidence to identify high-quality reasoning paths for self-training, supported by our empirical observations. We then propose a new self-training method, CORE-PO, that fine-tunes LLMs to prefer high-COnfidence REasoning paths through Policy Optimization. Our experiments show that CORE-PO improves the accuracy of outputs on four in-distribution and two out-of-distribution benchmarks, compared to existing self-training methods.