Selection Bias Can Trigger Model Collapse in Recursive Synthetic Training
The paper shows that using data selection to prevent model collapse can itself cause collapse when the verifier’s reference distribution is biased, as happens in siloed datasets like healthcare or finance. It proves the effect theoretically and offers a collaborative Wasserstein proxy to preserve diversity.