Rather than splitting based on a predefined list you can check with a text editor, BLTs use a neural net to tokenize. So it will be pretty hard to debug.
Basically, prior to feeding text to the tokenizer people have split the text on whitespaces. But whitespaces aren't exactly meaningful separators. By getting rid of this restriction as the tokenizer is 'learning', some of the tokens end up being 'by the way' or 'in the the long run.' The researchers find that this makes the model much more efficient.
Looks like parallel invention. (I’m not associated with the paper or its authors.)
In Boundless BPE, no schedule must be chosen, because there is not any point at which the constraints of pretokenization are removed entirely. Instead, at any point in the learning process, merges between adjacent pretokens are permitted if the pretokens are each represented by a single token. There are some additional details about how the authors incorporate Picky BPE, which I will not try to repeat because I would probably get them wrong.