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When Sample Selection Bias Precipitates Model Collapse

Summary

In low-resource data silos, using local reference distributions for data selection can accelerate model collapse instead of preventing it, leading to power-law diversity decay. Collaborative Wasserstein proxy references, constructed without sharing raw data, mitigate diversity degradation.

SourcearXiv AIAuthor: Xinbao Qiao, Xianglong Du, Wei Liu, Jingqi Zhang, Peihua Mai, Meng Zhang, Yan Pang
When Sample Selection Bias Precipitates Model Collapse
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[2606.13732] When Sample Selection Bias Precipitates Model Collapse

[Submitted on 11 Jun 2026]

Title:When Sample Selection Bias Precipitates Model Collapse

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Abstract: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 inherently incomplete. As a result, selection preferentially retains samples aligned with the local manifold while pruning globally relevant tail modes, turning from a safeguard against collapse into a mechanism that precipitates it. We theoretically prove that such siloed selection accelerates collapse and induces power-law diversity decay. As an initial mitigation, we construct Wasserstein proxy references from multiple silos without sharing raw data. Empirical results confirm that local-reference selection fails on skewed distributions, whereas collaborative proxy references mitigate diversity degradation, suggesting that recursive synthetic-data pipelines require particular caution when real-data coverage is fragmented or scarce.

Comments: Accepted at the 43rd International Conference on Machine Learning (ICML 2026)

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2606.13732 [cs.AI]

(or arXiv:2606.13732v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.13732

arXiv-issued DOI via DataCite

Submission history

From: Qiao Xinbao [view email] [v1] Thu, 11 Jun 2026 11:49:39 UTC (11,074 KB)

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Key points and analysis

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Key points

  • Recursive training on synthetic data risks model collapse; data selection is intended as a remedy.
  • In low-resource data silos (e.g., healthcare consortia, financial institutions), local reference distributions are biased, causing selection to favor local manifold while pruning global tail modes.
  • Theoretical proof shows that siloed selection accelerates collapse and induces power-law diversity decay.
  • Wasserstein proxy references built from multiple silos without sharing raw data mitigate diversity degradation.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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