The loss landscape mystery: how overparameterized models avoid overfitting

Modern deep neural networks routinely operate in an overparameterized regime — containing far more trainable parameters than training examples — yet consistently generalize well to unseen data. This directly contradicts classical statistical learning theory, which predicts that fitting more parameters than data points should result in overfitting and poor out-of-sample performance.