ASTER: Adaptive Dynamic Layer-Skipping for Efficient Transformer Inference via Markov Decision Process

Transformer-based models have demonstrated remarkable performance in computer vision tasks. However, their increasing model size leads to substantial memory demands and higher latency, hindering practical deployment. This paper presents an adaptive dynamic layer-skipping framework based on Markov Decision Process, which determines optimal computational paths based on the current state of input samples. We introduce a Temporal Importance Difference Reward mechanism to address the credit assignment problem in layer-skipping decisions, and develop a knowledge distillation strategy using learnable cognitive tokens to compensate for information loss. Experiments on various models demonstrate that our method significantly reduces computational costs while maintaining accuracy, offering a practical solution for deploying high-performance Transformer models in resource-constrained environments. The code is available at https://github.com/wjjkhl/ASTER