sample-selection-bias

This paper demonstrates that data selection in low-resource verification regimes, where verifiers only have access to fragmented and biased slices of the target distribution, can paradoxically accelerate model collapse by pruning globally relevant tail modes. The authors provide theoretical proof and propose a collaborative proxy reference mechanism as a mitigation strategy.