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arXiv · 2602.22014

A Diversity Diet for a Healthier Model: A Case Study of French ModernBERT

Louis Estève·Christophe Servan·Thomas Lavergne·Agata Savary
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Abstract

Diversity has been gaining interest in the NLP community in recent years. At the same time, state-of-the-art transformer models such as ModernBERT use very large pre-training datasets, which are driven by size rather than by diversity. This summons for an investigation of the impact of diversity on the ModernBERT pre-training. We do so in this study, with the express intent of reducing pre-training dataset size, while retaining at least comparable performance. We compare diversity-driven sampling algorithms, so as to pick the best one. We find that diversity-driven sampling allows in some tasks to gain 10 points relative to randomly-sampled pre-training data of commensurate size. We also see that a model pre-trained for 483h on a diversity-driven dataset of 150M tokens can yield a commensurate performance to a model pre-trained for 1,775h on a randomly-driven dataset of 2.4B tokens.

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Louis Estève, Christophe Servan, Thomas Lavergne, Agata Savary. 2026-02-25. A Diversity Diet for a Healthier Model: A Case Study of French ModernBERT. https://arxiv.org/abs/2602.22014

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