MARIO: Math Reasoning With Code Interpreter Output -- A Reproducible Pipeline

Liao Minpeng, Luo Wei, Li Chengxi, Wu Jing, Fan Kai. Arxiv 2024

[Paper] [Code]    
Fine Tuning GPT Has Code Model Architecture Pretraining Methods Reinforcement Learning Survey Paper Training Techniques

Large language models (LLMs) have seen considerable advancements in natural language understanding tasks, yet there remains a gap to bridge before attaining true artificial general intelligence, especially concerning shortcomings in mathematical reasoning capabilities. We postulate that the inherent nature of LLM training, which focuses on predicting probabilities of next token, presents challenges in effectively modeling mathematical reasoning that demands exact calculations, both from data-driven and theoretical standpoints. In this paper, we address this challenge by enriching the data landscape and introducing a novel math dataset, enhanced with a capability to utilize a Python code interpreter. This dataset is derived from GSM8K and MATH and has been further refined through a combination of GPT-4 annotations, human review, and self-training processes, where the errors in the original GSM8K training set have been fixed. Additionally, we propose a tentative, easily replicable protocol for the fine-tuning of math-specific LLMs, which has led to a significant improvement in the performance of a 7B-parameter LLM on the GSM8K and MATH datasets. We are committed to advancing the field of mathematical reasoning in LLMs and, to that end, we have made source code for data generation / training / inference, and the model checkpoints publicly available at \url{https://github.com/MARIO-Math-Reasoning/MARIO}. We hope this will facilitate further research and development within the community.

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