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MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning

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arxiv 2310.03731 v1 pith:Y4KXRD6Q submitted 2023-10-05 cs.CL cs.AIcs.CVcs.LG

Ke Wang , Houxing Ren , Aojun Zhou , Zimu Lu , Sichun Luo , Weikang Shi , Renrui Zhang , Linqi Song
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Mingjie Zhan Hongsheng Li
This is my paper · ORCID
classification cs.CLcs.AIcs.CVcs.LG
keywords mathcodemathcodermodelslanguageopen-sourceproblemsreasoning
verification ladder T0 review T1 audit T2 compute T3 formal
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The recently released GPT-4 Code Interpreter has demonstrated remarkable proficiency in solving challenging math problems, primarily attributed to its ability to seamlessly reason with natural language, generate code, execute code, and continue reasoning based on the execution output. In this paper, we present a method to fine-tune open-source language models, enabling them to use code for modeling and deriving math equations and, consequently, enhancing their mathematical reasoning abilities. We propose a method of generating novel and high-quality datasets with math problems and their code-based solutions, referred to as MathCodeInstruct. Each solution interleaves natural language, code, and execution results. We also introduce a customized supervised fine-tuning and inference approach. This approach yields the MathCoder models, a family of models capable of generating code-based solutions for solving challenging math problems. Impressively, the MathCoder models achieve state-of-the-art scores among open-source LLMs on the MATH (45.2%) and GSM8K (83.9%) datasets, substantially outperforming other open-source alternatives. Notably, the MathCoder model not only surpasses ChatGPT-3.5 and PaLM-2 on GSM8K and MATH but also outperforms GPT-4 on the competition-level MATH dataset. The dataset and models will be released at https://github.com/mathllm/MathCoder.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Arrows of Math Reasoning Data Synthesis for Large Language Models: Diversity, Complexity and Correctness

    cs.CL 2025-08 reject novelty 6.0 of 10

    A program-assisted pipeline generates 12.3 million math problem-solution pairs with execution-based verification, and fine-tuning on a 50k sample improves model scores on GSM8K, MATH, Minerva, and SVAMP.

  2. Teaching LLM to Reason: Reinforcement Learning from Algorithmic Problems without Code

    cs.CL 2025-07 conditional novelty 6.0 of 10

    TeaR uses GRPO reinforcement learning on test-case output prediction for algorithmic problems, with no code shown, and reports broad reasoning gains across 17 benchmarks.

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    A 3B agent trained with supervised tool-use traces and reinforcement learning answers week-long egocentric video questions by dynamically selecting hierarchical retrieval, video-LLM, and VLM tools.

  4. Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Users who first used a Spanish AI writing assistant subsequently used the English AI writing assistant less, suggesting a spillover that violates choice independence.

  5. A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs

    cs.LG 2026-02 reject novelty 5.0 of 10

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    cs.CL 2025-05 conditional novelty 5.0 of 10

    LLM hidden states encode enough information to predict chain-of-thought success before any reasoning tokens are generated, outperforming a text-only classifier.

  8. Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training

    cs.AI 2025-05 conditional novelty 5.0 of 10

    Reinforcing the two experts most correlated with thinking tokens improves reasoning accuracy and efficiency in MoE large reasoning models, with gains of up to 10 points on AIME benchmarks.

  9. DuaShepherd: Integrating Stepwise Correctness and Potential Rewards for Mathematical Reasoning

    cs.CL 2025-06 conditional novelty 4.0 of 10

    A reward model that multiplies stepwise correctness and potential scores improves best-of-N verification accuracy for math reasoning.

  10. Survey of Specialized Large Language Model

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    A survey of 24 specialized LLMs (2022-2025) claims a shift from domain fine-tuning to native architectures, but the synthesis is undermined by citation errors and selection bias.

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