AWS: Advanced SFT data strategies

The guide addresses the common challenge of optimizing SFT data beyond basic formatting. It emphasizes that more data isn't always better, citing findings like 128 epochs on 400 reasoning examples beating single-epoch training on 51,200 examples. This suggests focusing on data quality and diversity rather than sheer volume. The practicality of these strategies is highlighted by the learning curve analysis, which allows teams to find saturation without repeated training runs. The guide also cautions that data mixing primarily preserves general capabilities, not boost task performance, and advises monitoring token-level ratios to avoid gradient dominance. Overall, it provides a framework for efficient and effective data preparation, applicable to any model.