Synthetic Training Data: Generating It Without Poisoning the Model

Generating 50,000 training examples costs roughly a thousandth of annotating them, and cheapness is the least interesting thing about it. What decides whether the result works is a set of properties nobody notices until the model is trained: whether the generator holds the information at all, whether a checker can lift the ceiling above it, whether the dataset covers its input space or repeats one example, and whether the real data ever leaves the mix. This path builds the pipeline that survives all four.