Optimizing Neural Network Pruning with Multi-Objective Diffusion Reinforcement Learning

Structured pruning has become a mainstream approach for network lightweighting due to its advantages in terms of compression efficiency and deployment friendliness. Existing methods generally struggle to generate a complete Pareto front and cannot easily incorporate practical inference performance metrics directly into the optimization objective. This significantly constrains their capacity to recommend pruning subnet schemes and prevents them from adequately satisfying the actual requirements of specific tasks. To address the aforementioned challenges, we propose DRL-Pruner, a pruning framework guided by multi-objective reinforcement learning that caters to diverse budget optimization requirements. Leveraging the powerful generative capability of diffusion model, this method gradually iteratively refines randomly initialized pruning strategy into compression schemes that can fully cover the entire objective space. We statistically measure data such as the global computational variation and structural sensitivity of the network as state representation, providing the pruning framework with an observation set of structural states and performance trends. For remote sensing vision tasks, we conduct pruning experiments on ResNet-50, ViT-Tiny, and Faster R-CNN under the scene classification benchmark NaSC-TG2 and the object detection benchmark NWPU VHR-10 (v2). The results demonstrate that DRL-Pruner achieves an effective tradeoff between network accuracy and various inference budgets, providing pruning decisions that cover the Pareto front.