Nice :) I did something similar a few days ago. What I ended up with was a 50/50 blend of hilarious nonsense, and verbatim snippets.There seemed to be a lot of chains where there was only one possible next token.

I'm considering just deleting all tokens that have only one possible descendant, from the db. I think that would solve that problem. Could increase that threshold to, e.g. a token needs to have at least 3 possible outputs.

However that's too heavy handed: there's a lot of phrases or grammatical structures that would get deleted by that. What I'm actually trying to avoid is long chains where there's only one next token. I haven't figured out how to solve that though.

That's where a dynamic n-gram comes into play. Train the markov model from 1 to 5 n-grams, and then scale according to the number of potential paths available.

You'll also need a "sort of traversal stack" so you can rewind if you get stuck several plies in.