The Slingshot Effect: A Late-Stage Optimization Anomaly in Adam-Family of Optimization Methods
Adaptive gradient methods, notably Adam, have become indispensable for optimizing neural networks, particularly in conjunction with Transformers. In this paper, we present a novel optimization anomaly called the Slingshot Effect, which manifests during extremely late stages of training. We identify a distinctive characteristic of this phenomenon through cyclic phase transitions between stable and unstable training …