Potential issues:
- Compounding errors: If not carefully managed, errors in synthetic data can amplify across generations.
- Distribution drift: Synthetic data may not perfectly capture the nuances of real-world distributions.
- Overfitting: Models trained exclusively on synthetic data may struggle with real-world generalization.
- Loss of subtle patterns: Some intricate real-world patterns might be lost in synthetic representations.