Which Tokens Should SFT Actually Learn? A Token-Trimming Perspective on Mathematical Reasoning
Supervised fine-tuning (SFT) applies a uniform cross-entropy loss to all target tokens, even though different tokens provide unequal learning signals for mathematical reasoning. This uniform treatment can over-sharpen already mastered tokens while amplifying learning pressure on uncertain, low-confidence tokens, leading to suboptimal training dynamics. We propose Trimmed Logit-Gap SFT (TrimSFT), a simple token-level reweighting method that scales the SFT loss according to the logit gap between the gold token and its strongest competitor. TrimSFT trims supervision away from both extremes: tokens already mastered (large logit gap) and tokens weakly supported by the current model (small or n...
| Authors | Yaning Jia, Chunhui Zhang, Wenxuan Xu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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