TREX: Tokenizer Regression for Optimal Data Mixture

• URL: http://arxiv.org/abs/2601.13588v1
• Date: Tue, 20 Jan 2026 04:41:09 GMT
• Title: TREX: Tokenizer Regression for Optimal Data Mixture
• Authors: Inho Won, Hangyeol Yoo, Minkyung Cho, Jungyeul Park, Hoyun Song, KyungTae Lim,
• Abstract summary: Tokenizer Regression for Optimal Data MiXture (TREX) is a regression-based framework that efficiently predicts the optimal data mixture for tokenizer training.<n>TREX trains small-scale proxy tokenizers on random mixtures, gathers their compression statistics, and learns to predict compression performance from data mixtures.<n>TReX's predicted mixtures outperform mixtures based on LLaMA3 and uniform distributions by up to 12% in both inand out-of-distribution compression efficiency.
• Score: 10.917621429052183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Tokenizer Regression for Optimal Data MiXture (TREX), a regression-based framework that efficiently predicts the optimal data mixture for tokenizer training. 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 inand out-of-distribution compression efficiency, demonstrating strong scalability, robustness, and practical effectiveness.

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