Question

You are managing an Azure OpenAI fine-tuning job for a customer-support classification assistant. After the run completes, you review the training metrics and observe that training loss continues to decrease steadily across all 8 epochs, but validation loss stops improving after epoch 3 and begins to rise slightly. The deployed fine-tuned model performs noticeably worse on held-out real support tickets than a checkpoint you evaluated earlier. What is the most appropriate corrective action for the next fine-tuning run?

Accepted Answer

Reduce the number of epochs so training stops near the point where validation loss was lowest.

Answer

Increase the number of epochs to give the model more time to generalize to the validation set.

Answer

Remove the validation split entirely so the model trains on all available data each epoch.

Answer

Raise the learning rate multiplier to accelerate convergence on the training data.