Unifying Learning Dynamics and Generalization in Transformers Scaling Law
Researchers have developed a theoretical framework to unify the understanding of learning dynamics and generalization in transformer models. This work formalizes transformer training as an ordinary differential equation system, approximating it to kernel behaviors. The analysis reveals a two-stage scaling law for generalization error, with an initial exponential decay followed by a power-law decay after a resource threshold is met, proving this two-stage law to be tight. AI
IMPACT Provides a theoretical foundation for understanding and predicting transformer performance as resources scale.