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Ambient Dataloops introduces a dataset-model co-evolution framework that iteratively refines training data to enhance diffusion models, addressing the challenge of heterogeneous sample quality in modern datasets. By treating synthetically improved samples as progressively less noisy, this approach avoids destructive feedback loops while achieving state-of-the-art results in image generation and protein design. The method provides both empirical performance gains and theoretical justification for leveraging iterative data looping in generative modeling.
Ambient Dataloops: Generative Models for Dataset Refinement
arXiv:2601.15417v2 Announce Type: replace-cross Abstract: We propose Ambient Dataloops, an iterative framework for refining datasets that makes it easier for diffusion models to learn the underlying data distribution. Modern datasets contain samples of highly varying quality, and training directly on such heterogeneous data often yields suboptimal models. We propose a dataset-model co-evolution process; at each iteration of our method, the dataset becomes progressively higher quality, and the model improves accordingly. To avoid destructive self-consuming loops, at each generation, we treat the synthetically improved samples as noisy, but at a slightly lower noisy level than the previous iteration, and we use Ambient Diffusion techniques for learning under corruption. Empirically, Ambient Dataloops achieve state-of-the-art performance in unconditional and text-conditional image generation and de novo protein design. We further provide a theoretical justification for the proposed framework that captures the benefits of the data looping procedure.
Abstract:We propose Ambient Dataloops, an iterative framework for refining datasets that makes it easier for diffusion models to learn the underlying data distribution. Modern datasets contain samples of highly varying quality, and training directly on such heterogeneous data often yields suboptimal models. We propose a dataset-model co-evolution process; at each iteration of our method, the dataset becomes progressively higher quality, and the model improves accordingly. To avoid destructive self-consuming loops, at each generation, we treat the synthetically improved samples as noisy, but at a slightly lower noisy level than the previous iteration, and we use Ambient Diffusion techniques for learning under corruption. Empirically, Ambient Dataloops achieve state-of-the-art performance in unconditional and text-conditional image generation and de novo protein design. We further provide a theoretical justification for the proposed framework that captures the benefits of the data looping procedure.
29 pages, 11 figures, 17 tables. Accepted at ICML 2026
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2601.15417 [cs.LG]
(or arXiv:2601.15417v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2601.15417 Focus to learn more arXiv-issued DOI via DataCite
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