OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset

TL;DR

This paper introduces OpenMathInstruct-1, a large open-source math instruction dataset with 1.8 million problem-solution pairs, enabling open models to achieve competitive math reasoning performance.

Contribution

It presents a new large-scale open-source math dataset created using open LLMs, bridging the performance gap with closed-source models.

Findings

OpenMath-CodeLlama-70B achieves 84.6% on GSM8K

The dataset enables open models to perform competitively in math reasoning

Code, models, and dataset are publicly released under permissive license

Abstract

Recent work has shown the immense potential of synthetically generated datasets for training large language models (LLMs), especially for acquiring targeted skills. Current large-scale math instruction tuning datasets such as MetaMathQA (Yu et al., 2024) and MAmmoTH (Yue et al., 2024) are constructed using outputs from closed-source LLMs with commercially restrictive licenses. A key reason limiting the use of open-source LLMs in these data generation pipelines has been the wide gap between the mathematical skills of the best closed-source LLMs, such as GPT-4, and the best open-source LLMs. Building on the recent progress in open-source LLMs, our proposed prompting novelty, and some brute-force scaling, we construct OpenMathInstruct-1, a math instruction tuning dataset with 1.8M problem-solution pairs. The dataset is constructed by synthesizing code-interpreter solutions for GSM8K and MATH, two popular math reasoning benchmarks, using the recently released and permissively licensed Mixtral model. Our best model, OpenMath-CodeLlama-70B, trained on a subset of OpenMathInstruct-1, achieves a score of 84.6% on GSM8K and 50.7% on MATH, which is competitive with the best gpt-distilled models. We release our code, models, and the OpenMathInstruct-1 dataset under a commercially permissive license.