Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization
arXiv:2606.26002v1 Announce Type: new Abstract: 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 Inform
The increasing scale and computational demands of deep neural networks necessitate advanced compression techniques to maintain efficiency and deployability.
This development offers a significant step towards optimizing AI model deployment, especially in resource-constrained environments, by making large models more efficient to run.
The ability to more effectively compress AI models through hierarchical reinforcement learning reduces the computational and memory footprint, enabling broader application and potentially lowering operational costs.
- · AI developers
- · Edge computing providers
- · Device manufacturers
- · AI-powered SaaS companies
- · Providers of inefficient AI inference hardware
More powerful AI models can be deployed on less powerful and more ubiquitous hardware.
This could accelerate the adoption of complex AI in new sectors, reducing barriers to entry for smaller players.
Increased accessibility of advanced AI might lead to a democratization of AI capabilities, shifting the competitive landscape.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG