HiReLC

HiReLC: Hierarchical Reinforcement Learning Framework for Joint Neural Network Pruning and Quantization

Researchers introduce HiReLC, a hierarchical ensemble-RL framework that automates joint quantization and structured pruning of deep neural networks. The system uses two-level agents — low-level agents selecting per-kernel compression configurations and high-level agents coordinating global budget allocation via Fisher Information-based sensitivity estimates. Experiments on Vision Transformers and CNNs achieve 5.99–6.72× parameter-storage compression with accuracy drops of 0.55–5.62% in most settings. The controller is architecture-agnostic, using a surrogate MLP and active learning loop to reduce policy evaluation cost.