How do loss landscapes evolve during training in deep neural networks?

Loss landscapes become smoother and more structured as training progresses.

At initialization, the loss landscape is highly irregular and high-dimensional. As training proceeds, optimization tends to move parameters into regions where gradients are more aligned and curvature becomes more stable. Empirical studies show that early training navigates chaotic regions, while later training settles into flatter basins that generalize better.