Why does reducing batch size sometimes improve generalization?

Small batches introduce gradient noise that helps escape sharp minima.

Smaller batch sizes lead to noisier gradient estimates, which act as regularization and help the optimizer avoid overfitting narrow minima. This often leads to better generalization on unseen data.