Training from scratch with the combined old and new datasets ensures balanced learning and avoids overfitting to patterns from the previously fine-tuned checkpoint. It leverages the base model’s generalizability, treats all data equally, and eliminates biases toward earlier fine-tuned data. Since the dataset is small (<50 hours), training from scratch is cost-effective, quick (under $10), and provides a fresh optimization process for better convergence and flexibility for future fine-tuning.