Research suggests byte-level AI models may outperform tokenized models long-term

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A September 2026 paper from the University of Washington and Meta FAIR argues tokenization in language models is a crutch. The research found tokenized models progress quickly early in training then plateau, while byte-level models develop more slowly but continue improving. In distillation experiments, a 1B parameter byte-level model outperformed a comparable tokenized model while requiring significantly less training data and storage. The findings suggest byte models could offer more equitable multilingual performance and pricing by processing raw text bytes directly.

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