A theoretical basis for model collapse in recursive training

Key Points

• Model collapse occurs in recursive training, producing distinct outcomes based on sample contributions.
• Two different asymptotic behaviours are identified associated with the presence of any external source.
• Understanding these behaviours can help refine recursive training methods to mitigate collapse.
• This theoretical basis provides insights for future research in generative models and their stability.