Spring 2026 Seminars

Provable Long-Range Benefits of Next-Token Prediction (02/06/2026)

Presenter: Xinyuan Cao

Why do modern language models, trained to do well on next-word prediction, appear to generate coherent documents and capture long-range structure? Here we show that next-token prediction is provably powerful for learning longer-range structure, even with common neural network architectures. Specifically, we prove that optimizing next-token prediction over a Recurrent Neural Network (RNN) yields a model that closely approximates the training distribution: for held-out documents sampled from the training distribution, no algorithm of bounded description length limited to examining the next k tokens, for any k, can distinguish between k consecutive tokens of such documents and k tokens generated by the learned language model following the same prefix. We provide polynomial bounds (in k, independent of the document length) on the model size needed to achieve such k-token indistinguishability, offering a complexity-theoretic explanation for the long-range coherence observed in practice.

Links:

• https://www.arxiv.org/abs/2512.07818

• https://x.com/CaoYouki/status/1998546806469538156