Generative AI for Math: Part I -- MathPile: A Billion-Token-Scale
  Pretraining Corpus for Math
Zengzhi Wang Rui Xia Pengfei Liu
High-quality, large-scale corpora are the cornerstone of building foundation
models. In this work, we introduce \textsc{MathPile}, a diverse and
high-quality math-centric corpus comprising about 9.5 billion tokens.
Throughout its creation, we adhered to the principle of ``\emph{less is
more}'', firmly believing in the supremacy of data quality over quantity, even
in the pre-training phase. Our meticulous data collection and processing
efforts included a complex suite of preprocessing, prefiltering, language
identification, cleaning, filtering, and deduplication, ensuring the high
quality of our corpus. Furthermore, we performed data contamination detection
on downstream benchmark test sets to eliminate duplicates. We hope our
\textsc{MathPile} can help to enhance the mathematical reasoning abilities of
language models. We plan to open-source different versions of \mathpile with
the scripts used for processing, to facilitate future developments in this
field.