Multi Epoch Training

Multi-epoch training, involving iterating over datasets multiple times during model training, is a subject of ongoing investigation in machine learning, particularly concerning its effectiveness compared to single-epoch approaches. Current research focuses on mitigating overfitting issues, especially in high-dimensional sparse data scenarios, often employing techniques like data augmentation and careful layer-wise training (e.g., focusing on embedding layers). These efforts aim to improve model performance and address challenges like catastrophic forgetting in continual learning and enhance privacy through machine unlearning, impacting various applications from click-through rate prediction to large language model training.