NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning
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-stage LLM-based reinforcement learning framework that combines supervised fine-tuning on demonstration trajectories with online policy optimization. A universal mixed-embedding interface combines ...
| Authors | Lingfeng Peng, Xiaohui Xie, Yang Bao et al. |
|---|---|
| Primary category | cs.NI |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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