A new mechanism design framework from Google AI Research optimizes synthetic data generation using first principles. The approach targets specific reasoning gaps in current models by mathematically structuring training sets. This reduces reliance on scarce human-labeled data. Practitioners can now generate higher-quality synthetic corpora to improve model logic without increasing raw data volume.