arXiv:2602.16601v2 Announce Type: replace-cross Abstract: Machine learning models are increasingly trained or fine-tuned on synthetic data. Recursively training on such data has been observed to significantly degrade performance in a wide range of tasks, often characterized by a progressive drift away from the target distribution. In this work, we theoretically analyze this phenomenon in the setting of score-based diffusion models. For a realistic pipeline where each training round uses a combination of synthetic data and fresh samples from the target distribution, we obtain upper and lower bo
The increasing reliance on synthetic data for training large AI models makes understanding and mitigating 'model collapse' a pressing research frontier, reflected in this new theoretical analysis.
This research provides crucial theoretical insights into a critical failure mode in advanced AI systems, potentially impacting the reliability and long-term viability of AI models trained on synthetic data.
Our understanding of the limitations and error propagation in diffusion models, fostering the development of more robust training methodologies for AI utilizing synthetic data.
- · AI researchers
- · AI model developers
- · Data scientists
- · AI models reliant solely on synthetic data
- · Companies with suboptimal synthetic data pipelines
Improved methods for training AI models using synthetic data will emerge, leading to more resilient and performant systems.
The findings could drive new standards and best practices for synthetic data generation and AI model auditing.
Increased trust and accelerated adoption of AI systems in sensitive applications where model integrity is paramount.
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Read at arXiv cs.LG