Self-Explore: Enhancing Mathematical Reasoning in Language Models with Fine-grained Rewards

• URL: http://arxiv.org/abs/2404.10346v4
• Date: Thu, 03 Oct 2024 03:55:41 GMT
• Title: Self-Explore: Enhancing Mathematical Reasoning in Language Models with Fine-grained Rewards
• Authors: Hyeonbin Hwang, Doyoung Kim, Seungone Kim, Seonghyeon Ye, Minjoon Seo,
• Abstract summary: Training on large amounts of rationales (i.e., CoT Fine-tuning) is effective at improving the reasoning capabilities of large language models (LLMs) We propose Self-Explore, where the LLM is tasked to explore the first wrong step within the rationale and use such signals as fine-grained rewards for further improvement. On the GSM8K and MATH test set, Self-Explore achieves 11.57% and 2.89% improvement on average across three LLMs compared to supervised fine-tuning (SFT)
• Score: 42.065997425172974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training on large amounts of rationales (i.e., CoT Fine-tuning) is effective at improving the reasoning capabilities of large language models (LLMs). However, acquiring human-authored rationales or augmenting rationales from proprietary models is costly and not scalable. In this paper, we study the problem of whether LLMs could self-improve their reasoning capabilities. To this end, we propose Self-Explore, where the LLM is tasked to explore the first wrong step (i.e., the first pit) within the rationale and use such signals as fine-grained rewards for further improvement. On the GSM8K and MATH test set, Self-Explore achieves 11.57% and 2.89% improvement on average across three LLMs compared to supervised fine-tuning (SFT). Our code is available at https://github.com/hbin0701/Self-Explore.

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