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arXiv · 2412.11699

CoinMath: Harnessing the Power of Coding Instruction for Math LLMs

Chengwei Wei·Bin Wang·Jung-jae Kim·Guimei Liu·Nancy F. Chen
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Abstract

Large Language Models (LLMs) have shown strong performance in solving mathematical problems, with code-based solutions proving particularly effective. However, the best practice to leverage coding instruction data to enhance mathematical reasoning remains underexplored. This study investigates three key questions: (1) How do different coding styles of mathematical code-based rationales impact LLMs' learning performance? (2) Can general-domain coding instructions improve performance? (3) How does integrating textual rationales with code-based ones during training enhance mathematical reasoning abilities? Our findings reveal that code-based rationales with concise comments, descriptive naming, and hardcoded solutions are beneficial, while improvements from general-domain coding instructions and textual rationales are relatively minor. Based on these insights, we propose CoinMath, a learning strategy designed to enhance mathematical reasoning by diversifying the coding styles of code-based rationales. CoinMath generates a variety of code-based rationales incorporating concise comments, descriptive naming conventions, and hardcoded solutions. Experimental results demonstrate that CoinMath significantly outperforms its baseline model, MAmmoTH, one of the SOTA math LLMs.

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Chengwei Wei, Bin Wang, Jung-jae Kim, Guimei Liu, Nancy F. Chen. 2024-12-16. CoinMath: Harnessing the Power of Coding Instruction for Math LLMs. https://arxiv.org/abs/2412.11699

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