When Sample Selection Bias Precipitates Model Collapse
The proliferation of recursive training on synthetic data can alleviate data scarcity but risks model collapse, where repeated training erodes distributional tails and homogenizes outputs. Data selection is widely viewed as a remedy, yet its reliability depends critically on the reference distribution used by the verifier. We show that in low-resource verification regimes, where each verifier observes only a small, fragmented, and biased slice of the target manifold, selection itself becomes biased. This situation naturally arises in low-resource data silos such as healthcare consortia or proprietary financial institutions, where raw data cannot be pooled and local references are inherent...
| Authors | Xinbao Qiao, Xianglong Du, Wei Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 June 2026 |
| Last updated at source | 2 July 2026 |
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