Such a model will always be less performant than one on tokens, as you're effectively switching to one byte per token. Solving this problem in code is much cheaper.
I don't mean switching to one byte per token, but switching to training on the token distribution that results from cutting off the input at arbitrary bytes. The bytes per token should be basically unchanged, as only the end gets a bit shorter.
Yeah I've tried that approach. The model ends up needing to learn every combination of tokens. For example, the word "apple" now has six bytes positions it can be split on and the model suddenly needs to learn that all six will yield the same output attention state.
It ends up being O(max token length) more complex and so you end up needing a proportionally larger model to accommodate it.