I was under the impression that log probabilities don't work like that / they aren't really useful to be interpreted as probabilities?

https://news.ycombinator.com/item?id=42684629

> the logits aren't telling you anything like 'what is the probability in a random sample of Internet text of the next token', but are closer to a Bellman value function, expressing the model's belief as to what would be the net reward from picking each possible BPE as an 'action' and then continuing to pick the optimal BPE after that (ie. following its policy until the episode terminates). Because there is usually 1 best action, it tries to put the largest value on that action, and assign very small values to the rest (no matter how plausible each of them might be if you were looking at random Internet text)