Reflection-Driven Self-Improving Language Model Agents for Adaptive RAN Slicing

Abstract:Adaptive control in AI-native networks remains challenging because tasks such as dynamic Radio Access Network (RAN) slicing require persistent trade-off decisions among spectrum efficiency, service quality, and reconfiguration stability. Conventional Reinforcement Learning (RL) compresses these competing objectives into handcrafted scalar rewards, requiring exhaustive tuning for a balanced trade-off. Language model (LM) agents provide a new path toward adaptive control because they can semantically represent complex preference relations, but prompt-based adaptation struggles to retain long-horizon interaction history due to finite context limits. We propose a Reflection-Driven Self-Improvement (ReDSI) framework that replaces scalar rewards with semantic feedback for continuous policy adaptation. This framework is built upon a Small Language Model (SLM) agent for fast decision-making, coupled with a bi-perspective reflection to transform interaction history into preference data, subsequently distilling long-horizon decision experience into the SLM parameters through preference-based fine-tuning. Evaluated on a dynamic RAN slicing task, our method achieves an optimal trade-off across multiple competing objectives, outperforming standard RL baselines and existing LM-based agents by demonstrating substantially greater sample efficiency. These results highlight a viable paradigm of self-improving LM agents for reward-free multi-objective continuous control in AI-native networks.

Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)

arXiv:2603.10564 [cs.AI]

(or arXiv:2603.10564v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2603.10564 Focus to learn more arXiv-issued DOI via DataCite

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