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arXiv · 2602.01227

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

Zhanming Shen·Zeyu Qin·Jiaqi Hu·Wentao Ye·Hao Chen·Xiaomeng Hu·Haokai Xu·Gang Chen·Yi R. Fung·Haobo Wang
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Abstract

The transition from fitting empirical data to achieving true human utility is fundamentally constrained by a granularity mismatch, where fine-grained autoregressive generation is often supervised by coarse or uniform signals. This position paper advocates Token Priority as the essential bridge, formalizing Supervised Fine-Tuning (SFT) not as simple optimization but as a precise distribution reshaping process that aligns raw data with the ideal alignment manifold. We analyze recent breakthroughs through this unified lens, categorizing them into two distinct regimes: Positive Priority for noise filtration and Signed Priority for toxic modes unlearning. We revisit existing progress and limitations, identify key challenges, and suggest directions for future research.

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Zhanming Shen, Zeyu Qin, Jiaqi Hu, Wentao Ye, Hao Chen, Xiaomeng Hu, Haokai Xu, Gang Chen, Yi R. Fung, Haobo Wang. 2026-02-01. Supervised Fine-Tuning Needs to Unlock the Potential of Token Priority. https://arxiv.org/abs/2602.01227

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