TReX: Tokenizer Regression for Optimal Data Mixture

Abstract

Building effective tokenizers for multilingual Large Language Models (LLMs) requires careful control over language-specific data mixtures. While a tokenizer’s compression performance critically affects the efficiency of LLM training and inference, existing approaches rely on heuristics or costly large-scale searches to determine optimal language ratios. We introduce \textbf{T}okenizer \textbf{Re}gression for Optimal Data Mi\textbf{X}ture (\text{TReX}), a regression-based framework that efficiently predicts the optimal data mixture for tokenizer training. \text{TReX} trains small-scale proxy tokenizers on random mixtures, gathers their compression statistics, and learns to predict compression performance from data mixtures. This learned model enables scalable mixture search before large-scale tokenizer training, mitigating the accuracy-cost trade-off in multilingual tokenizer design. Tokenizers trained with TReX’s predicted mixtures outperform mixtures based on LLaMA3 and uniform distributions by up to 12% in both in- and out-of-distribution compression efficiency, demonstrating strong scalability, robustness, and practical effectiveness.