Name the signal early stopping watches, and explain the 'patience' knob

**Early stopping** is a training-time safeguard that halts a fine-tuning run before the configured number of epochs if the model stops improving on a held-out signal. The signal is almost always validation loss: the cross-entropy of the model on a held-out subset of data that is not used for gradient updates. Sometimes the signal is a more direct metric like eval-set accuracy or a downstream task score, but validation loss is the cheapest and most common pick. The trainer evaluates this signal at a fixed cadence, usually every few hundred or thousand training steps. After each evaluation it compares the new value to the best value seen so far. If the new value is better, it updates the best value and keeps training. If the new value is worse or equal, it ticks a counter. **Patience** is the threshold for that counter. Patience 3 means the trainer tolerates three consecutive evaluations with no improvement before halting. The reason for tolerance is noise: a single bad evaluation could be just luck of the batch, not real plateau. Three in a row is a stronger signal that progress has actually stalled. The net effect is that you set generous max epochs and let early stopping pick the actual stopping point based on the model's behaviour rather than a guessed budget.

**Early stopping** halts training when a held-out signal, typically validation loss, stops improving over a configured tolerance window. It prevents both overfitting and wasted compute by letting the model itself signal when the marginal training is no longer paying off. The monitored signal must be on held-out data, not training data. Training loss almost always continues to decrease as the model memorises the training set, so early stopping against it would never fire even when the model is clearly overfitting. The validation set provides an honest signal of generalisation. **Patience** is the noise-tolerance parameter. Each evaluation that fails to improve over the best so far value increments a counter; each evaluation that improves resets it to zero. When the counter exceeds the patience threshold, training halts. A patience of 1 stops at the first non-improvement, which is too aggressive because validation loss is noisy. A patience of 3 to 5 is the common range. Higher patience tolerates longer plateaus, which matters for runs that pass through transient slowdowns before improvement resumes. Two related knobs typically appear with patience: `min_delta` (the smallest improvement that counts as real, useful when validation loss is noisy near the optimum) and `restore_best_weights` (whether to roll back to the checkpoint with the best validation loss when stopping, which is almost always what you want). Setting these three together gives a robust early-stopping configuration that handles realistic noisy validation curves.