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Source note3 min read

Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization

Source

arXiv

arxiv.org/abs/2606.26002v1

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Source

Plain-Language Brief

The paper introduces HiReLC, a hierarchical reinforcement learning framework that automates the joint quantization and structured pruning of deep neural networks. It operates at two levels: low-level agents (LLAs) handle block-specific configurations like bitwidth, pruning ratios, etc., while high-level agents (HLAs) manage global budget allocation via ensemble voting and Fisher Information-based sensitivity estimates. To reduce computational cost, an iterative active learning loop is used, which interleaves RL optimization with post-compression fine-tuning. A lightweight MLP surrogate is employed to amortize expensive evaluations, and a logit-MSE proxy during cold-start. The controller is architecture-agnostic and the experiments showed effective compression ratios of 5.99 - 6.72x with minor accuracy drops.

What the Document Says

  • The authors present HiReLC, a hierarchical reinforcement learning framework for automated joint quantization and structured pruning of deep neural networks.
  • The framework has two levels: low-level agents (LLAs) and high-level agents (HLAs).
  • LLAs operate independently per block and select per-kernel configurations over a multi-discrete action space.
  • HLAs coordinate global budget allocation via ensemble voting guided by Fisher Information-based sensitivity estimates.
  • An iterative active learning loop is used to reduce computational cost, interleaving RL optimization with post-compression fine-tuning.
  • A lightweight MLP surrogate and a logit-MSE proxy are employed in this process.
  • The controller is architecture-agnostic by design.

Key Concepts Mentioned

  • Hierarchical reinforcement learning
  • Neural network compression
  • Structured pruning
  • Quantization
  • Active learning
  • Fisher Information
  • Ensemble voting

Claims Made by the Document

  • HiReLC is effective in achieving parameter-storage compression ratios of 5.99 - 6.72x.
  • The approach results in a 3.83 % gain in one setting.

Evidence Used

  • Experiments across Vision Transformer and CNN benchmarks

Methods / Approach

  • Hierarchical reinforcement learning
  • Active learning
  • Policy evaluation
  • Surrogate-guided RL optimization
  • Post-compression fine-tuning

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The framework may struggle with complex neural network architectures or specific types of data not tested in the experiments.

Open Questions

  • How well does HiReLC perform on large and complex neural networks?
  • Does HiReLC maintain performance on specialized or uncommon datasets?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper doesn't explicitly discuss the complexity of the neural networks tested, nor the variety of datasets used.

Raw Abstract / Excerpt

We present HiReLC, a hierarchical ensemble-reinforcement learning framework for automated joint quantization and structured pruning of deep neural networks. The framework decomposes the compression search across two levels of abstraction: low-level agents (LLAs) operate independently per block, selecting per-kernel configurations over a multi-discrete action space spanning bitwidth, pruning keep-ratio, quantization type, and granularity, while high-level agents (HLAs) coordinate global budget allocation via ensemble voting guided by Fisher Information-based sensitivity estimates. To mitigate the computational cost of policy evaluation, an iterative active learning loop interleaves surrogate-guided RL optimization with post-compression fine-tuning, using a lightweight MLP surrogate to amortize expensive evaluations and a logit-MSE proxy during cold-start. The surrogate is used for reward shaping rather than as a replacement for final post-compression evaluation. The controller is architecture-agnostic by design, with a modular layer abstraction decoupling the RL environment from the underlying network topology. Experiments across Vision Transformer and CNN benchmarks demonstrate effective parameter-storage compression ratios of 5.99 - 6.72$\times$ with a 3.83 % gain in one setting and 0.55 - 5.62 % accuracy drops elsewhere, supporting hierarchical policy decomposition and sensitivity-aware guidance as practical design choices for joint neural network compression.