arXiv:2502.18049v5 Announce Type: replace-cross Abstract: Recent studies identified an intriguing phenomenon in recursive generative model training known as model collapse, where models trained on data generated by previous models exhibit severe performance degradation. Addressing this issue and developing more effective training strategies have become central challenges in generative model research. In this paper, we investigate this phenomenon within a novel framework, where generative models are iteratively trained on a combination of newly collected real data and synthetic data from the pr
The proliferation of generative AI models has brought the 'model collapse' problem to the forefront, as recursive training methods are common and scalable. This paper proposes a timely solution to a critical issue hindering the iterative improvement of such models.
Addressing model collapse is crucial for the long-term viability and self-improvement capabilities of advanced generative AI systems. Without stable recursive learning, the capabilities of AI agents and large models risk degradation over time.
This research introduces 'weighting-based stabilization' as a potential method to prevent performance degradation in recursively trained generative models. It shifts the paradigm from simply identifying the problem to proposing a concrete algorithmic solution.
- · AI research institutions
- · Generative AI developers
- · Companies relying on synthetic data augmentation
- · Researchers without solutions to model collapse
Generative models can now be trained more effectively and stably in a recursive manner, improving their quality and reducing performance decay.
This stabilization could accelerate the development of more complex AI agents that learn continuously from their own outputs and interactions.
Improved generative AI could lead to more sophisticated synthetic data generation, potentially reducing reliance on costly real-world data collection in various applications.
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Read at arXiv cs.LG