The largest models in the world are running out of high-quality human text. The industry's solution has been to generate synthetic data using LLMs—but LLMs are inherently probabilistic.

They guess. They hallucinate. When you train recursively on the unverified outputs of another model, you risk compounding errors—a phenomenon known as model collapse. While the training process and the diversity of the data play a massive role in preventing collapse, the reliability of the verifier is equally critical. Without a reliable verifier, synthetic data can degrade model performance.