Awesome AI Math Datasets
Awesome AI Math Datasets is a community-maintained index, one of the "awesome" lists on GitHub, that catalogues open datasets for building and testing maths-capable language models. It groups sources by purpose, such as pretraining corpora, problem-and-solution sets, and evaluation benchmarks.
Rather than hosting any data itself, it points you to the primary sources, which makes it a good starting point when you want to see what is available or find additions that are newer than a static directory can cover.
As a meta-resource it carries no single licence: each dataset it links to has its own terms, so check the source before you build on it.
Related sources
AMPS
A dataset of informal mathematics introduced alongside the MATH benchmark. It includes more than 100,000 Khan Academy problems with step-by-step solutions in LaTeX and over 5 million problems generated with Mathematica scripts, totalling around 23 GB.
AutoMathText
Around 200 GB of mathematical text compiled from websites, arXiv, and GitHub, drawing on OpenWebMath, RedPajama, and AlgebraicStack. Every piece of content carries a score from 0 to 1 for relevance, quality, and educational value, labelled automatically by the Qwen-72B model.
MegaMath
An open mathematics pretraining dataset curated from diverse, maths-focused sources, with over 300 billion tokens. Among the largest open maths corpora available.
NaturalProofs
A dataset of 32,000 theorem statements and proofs, 14,000 definitions, and 2,000 other pages including axioms and corollaries, drawn from ProofWiki, the Stacks Project, and mathematics textbooks.