Capability-Aligned Hierarchical Learning for Tool-Augmented LLMs
Tool learning enables LLMs to invoke external tools to accomplish tasks. Prior studies have demonstrated the effectiveness of a hierarchical structure: a high-level policy handles global planning and decomposes tasks into manageable sub-tasks, and a low-level policy focuses on invoking tools to solve these sub-tasks. However, these works typically optimize the high-level and low-level policies separately, leading to planner-executor misalignment and limiting LLM performance on tool-use tasks. In this paper, we propose a method called Capability-Aligned Hierarchical Learning (CAHL), which leverages RLVR to jointly optimize both policies, enabling better alignment between the high-level pla...
| Authors | Haotong Yang, Ting Long, Yi Chang |
|---|---|
| Primary category | cs.AI |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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