Reinforcement Learning‐Based Adaptive LLM Inference Scheduling for Chinese Language Education in Wireless Networks

The deployment of large language models in wireless educational environments faces significant challenges due to the tension between computational intensity and dynamic network constraints. This paper proposes a reinforcement learning‐based adaptive inference scheduling framework for Chinese language education systems that intelligently balances service quality and system efficiency under varying wireless conditions. We formulate the scheduling problem as a Markov Decision Process and employ a Double Deep Q‐Network algorithm to learn optimal policies for selecting between full model inference, lightweight model execution, and cached response strategies. Our framework incorporates a comprehensive Quality of Service assessment model. Experimental evaluation across four network conditions demonstrates superior performance. The proposed approach achieves strategy selection accuracy of 89.4%–95.8% while maintaining stable educational quality across dynamic network environments, providing an effective solution for deploying AI‐driven educational systems in resource‐constrained wireless networks.