Preparing data for supervised fine-tuning Part 1: Formatting and quality
Quality beats quantity in SFT: 1,000 curated examples can match models trained on far more data.
“A wrong demonstration gets imitated.”
AWS published a practical guide to supervised fine-tuning data preparation, covering the CPT→SFT→RFT post-training pipeline and emphasizing that data quality dominates quantity. The post cites LIMA and AlpaGasus research showing that curated small datasets can outperform larger noisy ones. While useful practitioner content, it is a how-to tutorial rather than a novel announcement or industry-shifting signal.