Now a good fraction of the post training (Most maybe at this point? Who knows?) is spent on RLVR (reinforcement learning on verifiable reward), in this kind of training you don't judge the token generated themselves, but the answer as a whole ("does this program compile and pass the tests", "is the proof valid"). That's where all of LLMs progress over the past two years in software and math come from.

So yes, they are still next token predictors at their core, but they stopped being mere stochastic parrots since they are now being trained on much more than just imitating their training data in a statistically plausible way.