Linear Regression

When Does Model Collapse Occur in Structured Interactive Learning?

This paper formalizes model collapse in multi-model interactive learning environments where models are trained on each other's synthetic outputs. The authors represent model interactions as directed graphs and derive necessary and sufficient conditions for collapse based on interaction graph topology. Finite-sample results for linear regression and asymptotic guarantees for general M-estimators are established, supported by numerical experiments.