Right, but BPE is designed to compress alphabetic languages (multiple letters per word), whereas logographic languages are already compressed (one or more words per symbol, but more net symbols). I suppose I don't get the reason behind obsessing over efficiency at this step and why it is necessary. What is the relationship between vocabulary size and model computational requirements? If the model input is ultimately an embedding of a fixed number of dimensions, does the token vocabulary size really make much practical difference?