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Fine-tuning over large pretrained language models (PLMs) has established manystate-of-the-art results. Despite its superior performance, such fine-tuningcan be unstable, resulting in significant variance in performance and potentialrisks for practical applications. Previous works have attributed suchinstability to the catastrophic forgetting problem in the top layers of PLMs,which indicates iteratively that fine-tuning layers in a top-down manner is apromising solution. In this paper, we first point out that this method does notalways work out due to the different convergence speeds of differentlayers/modules. Inspired by this observation, we propose a simplecomponent-wise gradient norm clipping method to adjust the convergence speedfor different components. Experiment results demonstrate that our methodachieves consistent improvements in terms of generalization performance,convergence speed, and training stability. The codebase can be found athttps://github.com/yangalan123/FineTuningStability.

Improving Stability of Fine-Tuning Pretrained Language Models via Component-Wise Gradient Norm Clipping