Rethinking Token Reweighting for SFT: Suppress, Reverse, and Extrapolate Learned Features
Supervised fine-tuning (SFT) learns most aggressively from tokens that the model deems least likely. This helps acquire new behaviors, but also amplifies noisy or conflicting supervision and can overwrite useful pretrained knowledge. Through a unified policy-loss view, we revisit existing token-reweighting methods and show that they assign nonnegative coefficients to demonstrated tokens. Consequently, they can suppress or amplify supervised updates, but cannot reverse harmful features once learned. Moreover, larger training weights do not amount to feature extrapolation, since they change the optimization trajectory rather than scale a fixed SFT direction. We argue that reversal and extra...
| Authors | Cunchun Li, Haonan He, Yifan Gao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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