Grokking Phenomenon

Grokking describes the surprising phenomenon where neural networks achieve high generalization accuracy on test data long after perfectly memorizing the training data, a period often characterized by initially poor test performance. Current research focuses on understanding the underlying mechanisms of this delayed generalization, exploring its occurrence across various model architectures (including MLPs, Transformers, and CNNs) and datasets, and investigating the role of factors like weight norms, feature learning, and optimization algorithms. This research is significant because it challenges existing theories of generalization and could lead to improved training strategies and a deeper understanding of neural network learning dynamics.