Synthetic data: training models with models

Around 2024–2025, lab papers and blogs described pipelines where one model generates problems/solutions to train another—especially in math and coding.

Benefit: cover long tails and curricula. Risk: model collapse if real-data diversity is lost.

Filter quality (verifiers, code runners, formal checkers) became as critical as the generator.