OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset
Authors: name: Shubham Toshniwal; name: Ivan Moshkov; name: Sean Narenthiran; name: Daria Gitman; name: Fei Jia; name: Igor Gitman
Published: 2024-02-15 18:26:11

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.