Researchers analyzed how tokenization affects language modeling performance across various languages. They compared two approaches to optimizing tokenization jointly with language modeling and found that it significantly alters the structure of tokens. One approach recovered morphologically aligned tokens, while the other produced longer tokens prioritizing efficiency. The study also found that tokenization behavior varies by language typology and that a tokenizer-free approach can improve downstream performance in natural language processing tasks. --- Why it matters: This research matters to AI engineers because it highlights the importance of optimizing tokenization jointly with language modeling, which can lead to improved model performance across languages. It also provides insights into how different approaches to tokenization affect downstream NLP tasks. Source: https://arxiv.org/abs/2608.17325