AI models collapse when trained on recursively generated data

Key Points

• Generative AI models experience severe functional collapse when trained repeatedly on recursively generated data instead of real human content.
• Recursive training causes output distributions to narrow rapidly, leading to irreversible loss of tail data information across iterations.
• Analysis of text and image models like stable diffusion highlights critical challenges in sustaining generative AI on web-scale synthetic data.