Overfitting — fitting the noise
When a model keeps improving on its training data while getting worse on held-out data, because it has started fitting accidents of the particular examples.
Visible as training and validation loss curves that separate (section 4.4). Remedies include more data, weight decay, dropout, a smaller model and early stopping.