Synthetic Training Data Quality Collapse: How Feedback Loops Destroy Your Fine-Tuned Models
You generate 50,000 synthetic instruction-following examples with GPT-4, fine-tune a smaller model on them, deploy it, and the results look great. Six months later, your team repeats the process — except this time you generate the examples with the fine-tuned model to save costs. The second model's evals are slightly lower, but within noise. You tune the next version the same way. By the fourth iteration, your model's outputs have a strange homogeneity. Users report it sounds robotic. It struggles with anything that doesn't fit a narrow template. Your most capable fine-tune has become your worst.
This is model collapse — the progressive, self-reinforcing degradation that happens when LLMs train on data generated by other LLMs. It is not a theoretical risk. It is a documented failure mode with measurable mechanics, and it is increasingly likely to affect teams that have normalized synthetic data generation without thinking carefully about the feedback dynamics.
