[didibus]

> predict what would come next in this text sampled from distribution X

It tries to learn the distribution of "useful" results either through verified rewards or human feedback. Then it encodes that in the network. When you run inference later, it samples or selects from that distribution.

Maybe it's a matter of interpretation. It's not predicting the next token based purely on the training corpus's distribution anymore, the RL process fine tunes that distribution so it predicts the next token that is closer to what was rewarded during RL. But as I see it, it's still predicting the next token, just from a reenforcement learned distribution instead of one found in a corpus of data.