On the Generalization of SFT: A Reinforcement Learning Perspective with
  Reward Rectification
Authors: name: Yongliang Wu, name: Yizhou Zhou, name: Zhou Ziheng, name: Yingzhe Peng, name: Xinyu Ye, name: Xinting Hu, name: Wenbo Zhu, name: Lu Qi, name: Ming-Hsuan Yang, name: Xu Yang

We present a simple yet theoretically motivated improvement to Supervised
Fine-Tuning (SFT) for the Large Language Model (LLM), addressing its limited
generalization compared to reinforcement learning (RL). Through mathematical
analysis, we reveal that standard SFT gradients implicitly encode a problematic
reward structure that may severely restrict the generalization capabilities of
model. To rectify this, we propose Dynamic Fine-Tuning (DFT), stabilizing
gradient updates for each token by dynamically rescaling the objective function
with the probability of this token. Remarkably, this single-line code change
significantly outperforms standard SFT across multiple challenging benchmarks
and base models, demonstrating greatly improved generalization. Additionally,
our approach shows competitive results in offline RL settings, offering an
effective yet simpler alternative. This work bridges theoretical insight and
practical solutions, substantially advancing SFT performance. The code will be
available at https://github.com/yongliang-wu/DFT.