Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization
HiReLC introduces a two-tier reinforcement learning approach to automate neural network compression, splitting the optimization problem between block-level agents that tune quantization and pruning parameters and global coordinators that allocate compression budgets using Fisher Information sensitivity analysis. The framework addresses a persistent pain point in model deployment: reducing inference cost without manual hyperparameter search. By coupling RL policy optimization with lightweight surrogate models, the work targets practitioners balancing model size, latency, and accuracy across heterogeneous hardware targets. This matters because efficient compression remains a bottleneck for edge deployment and cost-sensitive inference at scale.
Modelwire context
ExplainerThe key novelty is not just automating compression, but splitting the optimization into two RL layers with asymmetric responsibilities. Block-level agents handle local quantization/pruning decisions while global coordinators use Fisher Information to allocate budgets top-down, avoiding the instability that plagues flat RL approaches to this problem.
This connects directly to the June 24 arXiv work on multi-step RL tool-use stability. That paper showed RL systems collapse when probability distributions spike on control tokens, breaking structured execution. HiReLC sidesteps this by imposing hierarchical structure upfront, letting local agents operate within budget guardrails set by coordinators rather than learning coordination end-to-end. The supervisory signal here is Fisher Information sensitivity rather than explicit error examples, but the principle is the same: constrain the RL search space to prevent divergence.
If HiReLC achieves comparable compression ratios to manual hyperparameter search on standard benchmarks (ResNet-50, MobileNet) within the next six months, and if a major inference framework (TensorRT, CoreML, ONNX Runtime) ships native support for the method, that signals real adoption potential. Otherwise, watch whether the approach requires problem-specific tuning of the hierarchy depth or budget allocation strategy, which would limit its claimed generality across hardware targets.
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MentionsHiReLC · Fisher Information · reinforcement learning · neural network compression
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