research paper empirically investigates scaling laws for

Transformer-based language models. The authors find that performance

improves predictably with increases in model size, dataset size, and

training compute, following power-law relationships across several

orders of magnitude. Other architectural details have minimal impact.

Optimally efficient training involves using very large models with

relatively less data and stopping before convergence. The study also

explores overfitting and provides equations to predict performance and

optimal resource allocation.