6GAgentGym: Tool Use, Data Synthesis, and Agentic Learning for Network Management

arXiv:2603.29656v1 Announce Type: cross Abstract: 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 framewo