arXiv: Hierarchical Control Pairs an LLM Strategic Planner With RL Skill Policies

arXiv·low signal

'Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution' (arXiv 2606.20014, ~June 20) proposes a pretrained LLM acting as a centralized strategic controller that selects among specialized reinforcement-learning skill policies, splitting high-level planning from low-level execution. For builders: a useful mental model for hybrid agent systems where an LLM routes between deterministic or learned subroutines rather than trying to do everything in a single context window.