HiMAC: Hierarchical Macro-Micro Learning for Long-Horizon LLM Agents
Large language model (LLM) agents have recently demonstrated strong capabilities in interactive decision-making, yet they remain fundamentally limited in long-horizon tasks that require structured planning and reliable execution. Existing approaches predominantly rely on flat autoregressive policies, where high-level reasoning and low-level actions are generated within a single token sequence, leading to inefficient exploration and severe error propagation over extended trajectories. In this work, we propose HiMAC, a hierarchical agentic RL framework that explicitly decomposes long-horizon decision-making into macro-level planning and micro-level execution. HiMAC models reasoning as a str...
| Authors | Hongbo Jin, Rongpeng Zhu, Jiayu Ding et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 March 2026 |
| Last updated at source | 5 May 2026 |
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