Halting training when validation performance stops improving rather than running for a fixed number of epochs — the primary defence against overfitting in practice.
Early stopping is a training strategy that monitors a validation metric — typically F1, accuracy, or validation loss — after each evaluation interval and halts training automatically when the metric stops improving. Instead of training for a predetermined number of epochs, the model trains until further training provably makes things worse.
A patience parameter defines how many evaluation intervals without improvement to tolerate before stopping. Patience of 3 means training continues for 3 more evaluations after the best-seen metric, and stops if nothing beats that best. The model saved at the best checkpoint is the one that gets deployed — not the final state at stopping time, which may already have degraded.
DEJAN's grounding classifier training validated every 500 steps and loaded the best model at end of training. LinkjeBERT trained for up to 10 epochs with early stopping to catch the optimal checkpoint before overfitting set in.
Deciding in advance how many epochs to train requires guessing when the model will saturate. Early stopping replaces that guess with an empirical observation: keep training until the data tell you to stop. It also saves compute — if the model peaks at epoch 4 out of a planned 20, early stopping saves 16 epochs of wasted training.
