Overfitting

Overfitting means the model fits training data idiosyncrasies and generalizes poorly to new data. Signals: train loss ↓ while validation loss ↑.

Mitigations: more data, stronger regularization, dropout, early stopping, data augmentation, and smaller adapters for small corpora.

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