LP-SFT: Keeping Plausible Alternatives Alive in Supervised Fine-Tuning
Supervised fine-tuning (SFT) is widely used to adapt pretrained language models to downstream domains, but can over-specialize the model and degrade its pre-existing capabilities. Standard cross-entropy drives probability mass onto the observed target token and can make plausible alternatives that the pretrained model itself endorsed vanish. Using Shannon and Renyi entropies, we show that pretrained next-token distributions exhibit a regular multimodal structure, with entropy peaks corresponding to different numbers of plausible alternatives. Motivated by this observation, we propose LP-SFT, a Local-Preserving Supervised Fine-Tuning objective with two key design principles: it removes the...
| Authors | Yueyang Wang, Baolong Bi, Shuo Lu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 July 2026 |
| Last updated at source | 3 October 2026 |
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