Model Collapse on Synthetic Data

investigates the effects of training models on recursively generated data; finds that training on model-generated content can cause irreversible defects where the original content distribution disappears; shows that the effect, referred to as model collapse, occurs in LLMs, VAEs, and GMMs; while tested on smaller scale models (~100M params), the authors suggest this effect is highly likely to transfer to larger models over time.