How much synthetic training data is enough before quality plateaus?

We're generating synthetic QA pairs from our domain corpus to fine-tune a smaller model. We can generate as many as we want but quality of generation degrades as we get further from good seed examples. At what point does more synthetic data stop helping — and how are teams filtering synthetic examples to keep training signal high rather than just adding noise?