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High-quality, large-scale corpora are the cornerstone of building foundationmodels. In this work, we introduce \textsc{MathPile}, a diverse andhigh-quality math-centric corpus comprising about 9.5 billion tokens.Throughout its creation, we adhered to the principle of ``\emph{less ismore}'', firmly believing in the supremacy of data quality over quantity, evenin the pre-training phase. Our meticulous data collection and processingefforts included a complex suite of preprocessing, prefiltering, languageidentification, cleaning, filtering, and deduplication, ensuring the highquality of our corpus. Furthermore, we performed data contamination detectionon downstream benchmark test sets to eliminate duplicates. We hope our\textsc{MathPile} can help to enhance the mathematical reasoning abilities oflanguage models. We plan to open-source different versions of \mathpile withthe scripts used for processing, to facilitate future developments in thisfield.

Generative AI for Math: Part I -- MathPile: A Billion-Token-Scale Pretraining Corpus for Math