Intro

This chapter delves into a recent study that analyzes scaling laws in autoregressive generative models, contrasting traditional optimization techniques with broader performance trends. The findings reveal that test loss exhibits a predictable power law behavior as model and data scales increase, provided that key resources remain sufficient.