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arXiv · 2610.19685

Beyond Distribution Matching: Selective Margin Preservation for Supervised Fine-Tuning

Yuxuan Wang·Guangxiang Zhao·Xinyu Tan·Yahui Han·Xinxiang Yin·Zhonghai Wu·Xiangzheng Zhang·Lin Sun·Tong Yang
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Abstract

Supervised fine-tuning (SFT) changes logit margins among non-target tokens at teacher-forced prefixes. Matching a local reference distribution keeps those probabilities and also penalizes margin expansions. We ask whether a soft auxiliary can preserve selected local non-target margin relations without freezing that distribution. We introduce \textbf{Selective Rank-Preserving SFT (SRP-SFT)}, a soft margin target: a hinge on reference-scaled lower bounds for pairs from the reference's top-$K$ non-target tokens, applied at fixed teacher-forced prefixes. The hinge stops once the requested bound is met. We derive the KL cost of violating a margin, characterize the feasible region and its conditional utility, and give a finite-step condition for reducing aggregate violations. In one-epoch experiments on Qwen3-4B-Base and UltraFeedback, this target raises MATH-500 and code-generation scores over CE and LP-SFT at all three training seeds. At seed 1234, MATH-500 pass@1 rises from 49.56\% to 54.56\% and the four-metric code mean rises from 65.91\% to 69.50\% relative to LP-SFT. Ranking controls, held-out cache diagnostics, sensitivity sweeps, and six additional backbones show where these score changes appear.

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Yuxuan Wang, Guangxiang Zhao, Xinyu Tan, Yahui Han, Xinxiang Yin, Zhonghai Wu, Xiangzheng Zhang, Lin Sun, Tong Yang. 2026-10-14. Beyond Distribution Matching: Selective Margin Preservation for Supervised Fine-Tuning. https://arxiv.org/abs/2610.19685

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