Supervised Fine-Tuning Needs to Unleash the Potential of Token Priority

Supervised fine-tuning requires Token Priority to formalize SFT as a distribution reshaping process toward an ideal alignment manifold, rather than simple optimization. The authors categorize recent breakthroughs into two types, Positive Priority (filtering noise) and Signed Priority (forgetting harmful patterns), and review progress, limitations, key challenges, and future directions.