Recursively Trained Diffusion Models: Limiting Collapse Distribution and Spectral Characterization
arXiv:2606.13796v1 Announce Type: cross Abstract: Recursive training of generative models on their own outputs can lead to model collapse, a compounding drift away from the true data distribution. Existing theoretical works bound finite-round error accumulation in the context of diffusion models, but two questions remain open:~what distribution does the recursion converge to, and how fast? We answer both, isolating a mechanism distinct from imperfect learning: even with perfect score estimation and exact sampling, the early stopping of the reverse diffusion (required for numerical stability) d
This paper addresses fundamental theoretical questions about the inherent limitations and convergence properties of recursively trained diffusion models, a critical area given the rapid advancement of generative AI.
Understanding model collapse in recursively trained diffusion models is crucial for developing more robust and reliable generative AI, impacting its long-term stability and applicability.
This research provides a deeper theoretical understanding of model collapse, offering avenues for mitigation and better engineering practices in generative AI development.
- · AI researchers
- · Generative AI developers
- · Companies building on diffusion models
- · Developers ignoring theoretical underpinnings
- · Applications reliant on unbounded recursive training
Improved understanding of generative model limitations and failure modes.
Development of new architectural or training techniques to prevent or mitigate model collapse more effectively.
More stable and trustworthy autonomous AI systems, potentially accelerating their adoption in critical applications.
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Read at arXiv cs.LG