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

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ethics.ai (8 June 2026), “Capability-Aligned Hierarchical Learning for Tool-Augmented LLMs,” evidence record 1260, https://ethics.ai/record/1260 (originally published by arXiv).

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