Autonomous 6G network management requires agents that can execute tools, observe the resulting state changes, and adapt their decisions accordingly. Existing benchmarks based on static questions or scripted episode replay, however, do not support such closed-loop interaction, limiting agents to passive evaluation without the ability to learn from environmental feedback. This paper presents 6GAgentGym to provide closed-loop capability. The framework provides an interactive environment with 42 typed tools whose effect classification distinguishes read-only observation from state-mutating configuration, backed by a learned Experiment Model calibrated on NS-3 simulation data. 6G-Forge bootstraps closed-loop training trajectories from NS-3 seeds via iterative Self-Instruct generation with execution verification against the Experiment Model. Supervised fine-tuning on the resulting corpus followed by reinforcement learning with online closed-loop interaction enables an 8B open-source model to achieve comparable overall success rate to GPT-5 on the accompanying 6GAgentBench, with stronger performance on long-horizon tasks. Together, these components provide a viable path toward autonomous, closed-loop network management.

翻译：自主6G网络管理需要能够执行工具、观察结果状态变化并相应调整决策的智能体。然而，现有基于静态问题或脚本化场景回放的基准测试无法支持此类闭环交互，限制了智能体只能进行被动评估，而无法从环境反馈中学习。本文提出6GAgentGym以提供闭环能力。该框架提供了一个包含42种类型化工具的交互环境，其效果分类将纯观测与状态改变配置加以区分，并由基于NS-3仿真数据校准的学习型实验模型（Experiment Model）支撑。6G-Forge通过迭代式自指令生成（Self-Instruct）结合实验模型执行验证，从NS-3种子数据引导出闭环训练轨迹。对生成语料库进行监督微调，再结合在线闭环交互的强化学习，使得一个8B开源模型在配套的6GAgentBench上实现了与GPT-5相当的整体成功率，并在长周期任务上展现出更强性能。这些组件共同为迈向自主闭环网络管理提供了可行路径。