Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier

The academic paper "Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier" investigates the phenomenon of **error amplification** in **autoregressive sequence modeling**, particularly with **next-token prediction** and **imitation learning**, where model errors worsen with increased sequence length. The authors confirm that this amplification occurs when the **learning model is misspecified** and lacks the expressive power to represent the target distribution, leading to a **growing approximation factor (Capx)**. They explore whether this issue can be mitigated, revealing **inherent computational-statistical tradeoffs**. Their findings indicate that while **information theory** suggests error amplification is avoidable, next-token prediction inherently suffers from at least a **moderate increase in error (Capx = Ω(H))**. Furthermore, achieving better approximation factors for **autoregressive linear models** is computationally challenging, although there is a potential **trade-off between compute and statistical power** in specific scenarios.