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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.</abstract>
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%0 Conference Proceedings
%T CoinMath: Harnessing the Power of Coding Instruction for Math LLM
%A Wei, Chengwei
%A Wang, Bin
%A Kim, Jung-jae
%A Liu, Guimei
%A Chen, Nancy F.
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wei-etal-2025-coinmath
%X 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.
%R 10.18653/v1/2025.findings-acl.44
%U https://aclanthology.org/2025.findings-acl.44/
%U https://doi.org/10.18653/v1/2025.findings-acl.44
%P 786-797

Markdown (Informal)

[CoinMath: Harnessing the Power of Coding Instruction for Math LLM](https://aclanthology.org/2025.findings-acl.44/) (Wei et al., Findings 2025)

• CoinMath: Harnessing the Power of Coding Instruction for Math LLM (Wei et al., Findings 2025)

ACL

• Chengwei Wei, Bin Wang, Jung-jae Kim, Guimei Liu, and Nancy F. Chen. 2025. CoinMath: Harnessing the Power of Coding Instruction for Math LLM. In Findings of the Association for Computational Linguistics: ACL 2025, pages 786–797, Vienna, Austria. Association for Computational Linguistics.