Submitted:
07 February 2026
Posted:
09 February 2026
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Abstract
This study presents L2M2, a hierarchical framework in which LLMs generate high-level strategies while MARL agents execute low-level control policies. The architecture targets long-horizon coordination problems by decomposing decision-making across temporal scales. Evaluation on navigation and resource allocation tasks totaling 8,200 episodes shows that L2M2 improves task success rates by 20.5% and reduces convergence time by 1.6× compared to flat MARL approaches.
Keywords:
hierarchical reinforcement learning
; LLM-guided planning
; multi-agent systems
; longhorizon tasks
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