Study proposes token-trimming approach to supervised fine-tuning for math reasoning

A new arXiv paper argues that standard supervised fine-tuning applies its loss uniformly across all tokens, even though some are already mastered and others carry far more useful learning signal for mathematical reasoning. The authors introduce a token-trimming perspective that prioritizes which tokens a model should actually learn during fine-tuning, aiming to avoid over-sharpening well-understood tokens while strengthening the ones that matter most.