E-Ink News Daily

The article argues that characterizing LLMs solely as next-token predictors is an incomplete framing, especially for post-trained models. It contrasts pre-training, where models learn from existing sequences in training data, with RLVR (Reinforcement Learning with Verifiable Rewards), where models explore by generating novel sequences and learn from their outcomes rather than mimicking ground-truth continuations.