What factor should drive your decision on when to halt the fine-tuning process?

The primary factor that should drive the decision on when to halt the fine-tuning process is the performance of the model on a validation set. The validation set is a subset of the data that is held out from the training process and used to evaluate the model's generalization ability. During fine-tuning, the model's performance on the training set will typically improve over time, but this does not necessarily mean that the model is generalizing well to new, unseen data. If the model is trained for too long, it may start to overfit the training data, meaning that it memorizes the specific examples in the training set but performs poorly on new data. To avoid overfitting, it is crucial to monitor the model's performance on the validation set during fine-tuning. The fine-tuning process should be halted when the model's performance on the validation set starts to plateau or decline. This indicates that the model is no longer generalizing effectively and that further training will likely lead to overfitting. Monitoring metrics such as validation loss or task-specific evaluation metrics (e.g., accuracy, F1-score) can help determine the optimal stopping point.