Rotation-Preserving Supervised Fine-Tuning

This paper introduces Rotation-Preserving Supervised Fine-Tuning (RPSFT) to improve out-of-domain generalization in large language models while mitigating the degradation caused by standard SFT. RPSFT penalizes changes in projected singular subspaces of pretrained weights, acting as an efficient proxy for Fisher-sensitive directions and outperforming standard SFT baselines.