Researcher argues synthetic data accelerates overfitting and can't enable recursive self-improvement

Gerard Sans argues that synthetic data, as a product of inference, embeds training-corpus bias and accelerates overfitting toward model collapse; since training is compression with inevitable information loss, synthetic data cannot enable recursive self-improvement.

2026-10-21 ~ 2026-10-21 · 3 related posts

• Synthetic data's core flaw: embedded biases speed up overfitting and drive mode collapse — gerardsans · 2026-10-21
• Synthetic data pitfalls and a practical playbook for avoiding mode collapse — gerardsans · 2026-10-21
• Researcher argues synthetic data can't enable recursive self-improvement: compression loses information — gerardsans · 2026-10-21