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NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning

L5 · ResearcherResearcharXiv· 10/12/2026

Novel RL architecture combining supervised fine-tuning with online optimization for network systems, with significant performance improvements over existing methods.

AI Summary

Researchers propose NetRL, a two-stage LLM-based reinforcement learning framework that achieves state-of-the-art performance on network decision tasks like adaptive bitrate and load balancing.

Excerpt

Network decision-making tasks, including adaptive bitrate streaming, load balancing, and congestion control, require policies to process heterogeneous states and actions while balancing near- and long-term consequences. Existing solutions generalize poorly across network tasks, require substantial task-specific redesign, and lack reliable evaluation of long- horizon action consequences. Methods that learn through online interaction additionally face unstable optimization. We present NetRL, a two

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