Model collapse refers to degradation that can arise when generative models are repeatedly trained on data generated by earlier models. Common patterns may dominate while rare features and distribution tails are progressively lost.
Synthetic data is not inherently harmful; provenance, purpose, mixture, and validation matter. See Model Collapse as a Feedback Loop.