From my understanding this not only removes tokenization but also sampling correct? Sampling can be a pain point of LLMs, but they also can enable interesting

From my understanding this not only removes tokenization but also sampling correct? Sampling can be a pain point of LLMs, but they also can enable interesting usages, like forcing grammar so the model always outputs valid JSON or tuning temperature to get more varied distribution, XTC sampling, etc. What would be the equivalent of these in a BLT? I can only think of providing the decoder an extra input of allowed/prohibited bytes and run the decode over and over until it outputs something valid, maybe there's a simpler and more obvious approach.