Stop Thinking of LLMs as Next-Token Predictors

Large language models are fundamentally different from simple next-token predictors because they undergo reinforcement learning training that goes beyond predicting existing text. While the base model learns by predicting tokens from training data, post-trained LLMs also learn from new sequences they generate themselves and receive rewards for, making them exploratory systems rather than mere predictors of existing patterns.