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, leadin...
Ya-Ning Jia, Chun-Hui Zhang, Wen-Xuan Xu et al.
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