Synthetic data pitfalls and a practical playbook for avoiding mode collapse
gerardsans · x · 2026-10-21
Replying to Ramez's critique, Gerard Sans argues synthetic data—with embedded corpus biases—speeds up overfitting during fine-tuning and risks mode collapse. His playbook: prefer high-quality curated pre-training data; if impossible, use models with non-overlapping training corpora; if you must reuse the same model family, mix synthetic with real data and monitor data loss and overfitting.
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