The integration of Generative AI models into AI-native network systems offers a transformative path toward achieving autonomous and adaptive control. However, the application of such models to continuous control tasks is impeded by intrinsic architectural limitations, including finite context windows, the lack of explicit reward signals, and the degradation of the long context. This paper posits that the key to unlocking robust continuous control is enabling agents to internalize experience by distilling it into their parameters, rather than relying on prompt-based memory. To this end, we propose a novel self-finetuning framework that enables agentic systems to learn continuously through direct interaction with the environment, bypassing the need for handcrafted rewards. Our framework implements a bi-perspective reflection mechanism that generates autonomous linguistic feedback to construct preference datasets from interaction history. A subsequent preference-based fine-tuning process distills long-horizon experiences into the model's parameters. We evaluate our approach on a dynamic Radio Access Network (RAN) slicing task, a challenging multi-objective control problem that requires the resolution of acute trade-offs between spectrum efficiency, service quality, and reconfiguration stability under volatile network conditions. Experimental results show that our framework outperforms standard Reinforcement Learning (RL) baselines and existing Large Language Model (LLM)-based agents in sample efficiency, stability, and multi-metric optimization. These findings demonstrate the potential of self-improving generative agents for continuous control tasks, paving the way for future AI-native network infrastructure.

翻译：生成式人工智能模型与AI原生网络系统的集成为实现自主自适应控制提供了一条变革性路径。然而，此类模型在连续控制任务中的应用受到其固有架构限制的阻碍，包括有限的上下文窗口、缺乏显式奖励信号以及长上下文性能退化。本文认为，实现鲁棒连续控制的关键在于使智能体能够通过将经验蒸馏至其参数内部来实现经验内化，而非依赖基于提示的记忆机制。为此，我们提出了一种新颖的自微调框架，使智能体系统能够通过与环境的直接交互实现持续学习，无需人工设计奖励函数。该框架采用双视角反思机制，通过生成自主语言反馈从交互历史中构建偏好数据集。随后的基于偏好的微调过程将长周期经验蒸馏至模型参数中。我们在动态无线接入网切片任务上评估所提方法，该任务是一个具有挑战性的多目标控制问题，需要在波动的网络条件下解决频谱效率、服务质量与重配置稳定性之间的尖锐权衡。实验结果表明，我们的框架在样本效率、稳定性和多指标优化方面均优于标准强化学习基线及现有基于大语言模型的智能体。这些发现证明了自改进生成式智能体在连续控制任务中的潜力，为未来AI原生网络基础设施的发展开辟了道路。