Preparing data for supervised fine-tuning Part 2: Advanced data strategies
AWS recommends ~2,000 high-quality samples as baseline for supervised fine-tuning tasks
“plan for roughly 2,000 high-quality training samples for a typical SFT task”
AWS Machine Learning Blog shares practical benchmarks and techniques for SFT data optimization, including learning curve analysis to find dataset saturation points. The post provides concrete sample-size guidance (500 to 10,000+ depending on task complexity) and references Amazon Nova customization findings. Solid practitioner reference but no major announcement or industry shift.