Separate Reverse: A Gradient-Conflict-Free Training Framework for Multi-Exit Transformers

Abstract

Pretrained transformer models have demonstrated excellent performance on complex tasks. To improve their inference efficiency, recent studies have introduced the multi-exit mechanism, which enables early exiting through multiple intermediate classifiers. However, the deep architectures of pretrained transformers cause severe gradient conflicts during multi-exit fine-tuning, leading to degraded shallow-exit accuracy and reduced early-exit efficiency. To address this issue, we propose Separate Reverse, a multi-exit training strategy specifically designed for pretrained transformer models. The method iteratively integrates reverse iterative optimization and hierarchical knowledge distillation from deeper to shallower exits, maintaining pretrained parameter integrity, enhances the representation capacity of shallow exits, and coordinates gradient updates across exits to achieve a balanced optimization between shallow and deep classifiers. Experiments on multiple GLUE benchmark datasets using BERT demonstrate that our method significantly improves shallow-exit accuracy, maintains main-exit performance, and accelerates inference for simple samples by a large margin.