arXiv:2606.13732v1 Announce Type: new 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
The proliferation of synthetic data generation and recursive training in AI models is reaching a point where its inherent limitations, like sample selection bias and model collapse, are becoming critical research areas.
This research highlights a fundamental challenge in scaling AI with synthetic data, impacting the reliability and generalizability of future AI systems, especially in low-resource environments or with constrained data verification.
The understanding that even data selection, intended as a remedy for model collapse, can be a source of bias under certain conditions changes the approach to AI training and data curation strategies.
- · Researchers focused on robust AI training
- · Developers of bias detection and mitigation techniques
- · AI companies with diverse and high-quality real data sources
- · AI models heavily reliant on recursive training with synthetic data
- · Companies with limited access to diverse, verified real-world data
Increased focus on robust data verification and selection methods in AI development.
Potential for development of new AI architectures or training paradigms less susceptible to sample selection bias and model collapse.
Widening gap between AI systems trained with abundant, diverse real data and those constrained by synthetic or biased data sources, impacting competitive landscapes.
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Read at arXiv cs.AI