Hello Serhat,
One of the primary concerns associated with repetitive training is that the model tends to exhibit memorization rather than generalization. This is because running similar data through model can make the model remember the data. Consequently, issues like overfitting may arise.
To mitigate this problem, it is imperative to ensure that there is no data leakage, meaning that the training, validation, and test sets remain distinct to prevent overfitting. Employing techniques such as cross-validation and regularization can be highly effective in averting these issues.
Furthermore, another approach to combat overfitting is to consider adjusting the model architecture, simplifying it if necessary. This adjustment can help alleviate overfitting concerns. Additionally, maintaining a comprehensive log of training metrics, including loss and validation performance, for each training iteration can prove invaluable. Such a log facilitates the identification of when and where the model's performance begins to deteriorate.
