Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computing
Emerging computation-intensive applications impose stringent latency requirements on resource-constrained mobile devices. Mobile Edge Computing (MEC) addresses this challenge through task offloading. However, designing effective policies remains difficult due to dynamic task arrivals, time-varying channels, and the spatio-temporal coupling of server queues. Conventional heuristics lack adaptability, while Deep Reinforcement Learning (DRL) suffers from limited generalization and architectural rigidity, requiring retraining when network topology changes. Although Large Language Models (LLMs) offer semantic reasoning capabilities, standard Supervised Fine-Tuning (SFT) yields myopic policies ...
| Authors | Ning Yang, Chuangxin Cheng, Haijun Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 8 April 2026 |
| Last updated at source | 26 August 2026 |
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