When building tokenizers for language models, the search procedure (how you build the vocabulary) matters more than the optimization objective (what you're trying to optimize), at least for compression efficiency—but neither strongly predicts linguistic performance.
This paper investigates what makes tokenizers (algorithms that break text into pieces for language models) effective by separating two design choices: what they optimize for (compression vs. likelihood) and how they search (bottom-up vs. top-down).