RegMix: Data Mixture as Regression for Language Model Pre-training

ICLR 2025 Conference Submission6642 Authors

Keywords: language model pre-training, data mixture, regression

TL;DR: We introduce RegMix, an automated data mixture method that formulates data mixture as a regression problem. RegMix achieves a 6.3% improvement over human selection on the HellaSwag benchmark, with only a 2% extra training FLOPs.

Abstract: The data mixture for large language model pre-training significantly impacts performance, yet how to determine an effective mixture remains unclear. We propose RegMix to automatically identify a high-performing data mixture by formulating it as a regression task. RegMix involves training a set of small models with diverse data mixtures and fitting a regression model to predict their performance given their respective mixtures. With the fitted regression model, we simulate the top-ranked mixture and use it to train a large-scale model with orders of magnitude more compute. To empirically validate RegMix, we train 512 models with 1M parameters for 1B tokens of different mixtures to fit the regression model and find the optimal mixture. Using this mixture we train a 1B parameter model for 25B tokens (i.e. 1000x larger and 25x longer) which we find performs best among 64 candidate 1B parameter models with other mixtures. Further, our method outperforms both human selection and DoReMi in terms of both validation loss and downstream performance. Our experiments also show that (1) Data mixtures significantly impact performance with single-task performance variations of up to 14.6%; (2) Web corpora rather than data perceived as high-quality like Wikipedia have the strongest positive correlation with downstream performance; (3) Domains interact in complex ways often contradicting common sense, thus automatic approaches like RegMix are needed.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 6642