- Updated: March 11, 2026
- 1 min read
HiMAC: Hierarchical Macro-Micro Learning for Long-Horizon LLM Agents
We present an in‑depth overview of the HiMAC framework, a hierarchical macro‑micro learning approach that enables large language model (LLM) agents to tackle long‑horizon tasks with structured planning and reliable execution. HiMAC decomposes decision‑making into a high‑level planner that generates a blueprint and a low‑level executor that carries out goal‑conditioned actions. The paper introduces a critic‑free hierarchical policy optimisation method and an iterative co‑evolution training strategy, achieving state‑of‑the‑art results on ALFWorld, WebShop and Sokoban. For a full technical description, see the original arXiv paper. Learn more about our research and related projects at ubos.tech.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.