Flat convergence

Flat convergence generally refers to a concept in optimization and machine learning, particularly in the context of training neural networks. It describes a situation where the loss landscape of a model has regions where the loss does not change much, even with significant changes in the model parameters. In other words, a "flat" region in the loss landscape indicates that there are many parameter configurations that yield similar performance (loss values), as opposed to "sharp" regions where small changes in parameters lead to large changes in loss.