Sharpness Dynamic

Sharpness dynamics in neural network training focuses on understanding how the curvature of the loss landscape, specifically the largest eigenvalue of the Hessian matrix (sharpness), evolves during optimization. Current research investigates the relationship between sharpness and generalization performance, exploring how algorithms like Sharpness-Aware Minimization (SAM) can be improved to find flatter minima and enhance model robustness. This research is significant because it helps explain the observed transferability of hyperparameters across different model scales and sheds light on the optimization dynamics leading to improved generalization and efficient training of large models, impacting both theoretical understanding and practical applications like model quantization.