Loss Smoothing Improves Model Adaptation Under Distribution Shift

A new research paper introduces a technique called loss smoothing, designed to enhance the stability and performance of neural networks when adapting to new data distributions. This method involves a gradual transition between the initial training objective and the new target objective, rather than an abrupt shift. By interpolating these objectives at the start of adaptation, loss smoothing helps preserve valuable features learned from the original data while allowing the model to specialize effectively for the new task. The study demonstrates that this smoother objective transition consistently improves model performance across diverse applications, including supervised learning, vision adaptation, reinforcement learning, and language model fine-tuning. This suggests that a more gentle approach to objective changes during adaptation can be a widely beneficial strategy for machine learning practitioners.