The bitter lesson is coming for tokenization

The main limitation of tokenization is actually logical operations, including arithmetic. IIRC most of the poor performance of LLMs for math problems can be attributed to some very strange things that happen when you do math with tokens.

I'd like to see a math/logic bench appear for tokenization schemes that captures this. BPB/perplexity is fine, but its not everything.

regarding “math with tokens”: There was paper with tokenization that has specific tokens for int numbers, where token value = number. model learned to work with numbers as numbers and with tokens for everything else... it was good at math. can’t find a link, was on hugginface papers

Shouldn't production models already do this? They already tend to use tokenizers with complex rules to deal with a lot of input that would otherwise be tokenized in a suboptimal way. I recall a bug in an inference engine (maybe llama.cpp?) because of an implementation difference in their regex engine compared to the model trainer. Which means that the tokenizer used regex-based rules to chop up the input.