HiMAC: Hierarchical Macro-Micro Learning for Long-Horizon LLM Agents

Researchers introduce HiMAC, a hierarchical reinforcement learning framework that improves LLM agent performance on long-horizon tasks by separating macro-level planning from micro-level execution. The approach demonstrates state-of-the-art results across multiple environments, showing that structured hierarchy is more effective than simply scaling model size for complex agent tasks.