You fine-tune a GPT-4o model in Azure AI Foundry on a proprietary dataset of 800 curated examples. After training, the model performs excellently on the training set but produces rigid, low-quality responses on held-out validation prompts, and the training loss curve dropped rapidly while validation loss increased after the first epoch. You must adjust the fine-tuning hyperparameters to improve generalization. Which change is the most appropriate first step?