Stanford Researchers Propose Scaling Laws for Synthetic Data Rewriting
Percy Liang's group released a paper using scaling laws to quantify how much compute should be spent on synthetic data rewriting, finding the optimal fraction of synthetic data rises to about 32% as compute increases.
2026-10-31 ~ 2026-10-31 · 3 related posts
- How much compute should data rewriting get? New scaling-law study quantifies it — ChengleiSi · 2026-10-31
- Scaling laws quantify how much compute synthetic data rewrites are worth — chrmanning · 2026-10-31
- Stanford paper proposes rewriting scaling law: optimal synthetic data share rises to 32% with compute — stanfordnlp · 2026-10-31