"Next-token predictor" is the wrong mental model for LLMs

Garrin McGoldrick’s argument is that calling a model a next-token predictor describes the architecture accurately and the training objective badly, and that people reach for it precisely when they are making claims about the second.

The distinction is between the two phases. In pre-training, the token that actually came next in the data becomes more likely, so prediction is the right word. Under reinforcement learning with verifiable rewards it is not: the model explores a sequence, the sequence earns a reward, and the tokens in it become more likely because of that reward. As he puts it, there is no next token to get right, only a reward to maximise.

The chess analogy carries it. A system trained to imitate grandmaster moves is a move predictor. A system that explores and learns which positions win is doing something else, even where the two share an architecture. His conclusion is that post-trained models keep the appearance of next-token prediction while encoding behaviour that came from somewhere other than imitation, so the mental model quietly swaps the implementation for the objective.