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AI Research
arXiv CS.AI·Jun 9·5 min read
Capability-Aligned Hierarchical Learning for Tool-Augmented LLMs
TL;DR
Prior approaches to tool learning in large language models (LLMs) often optimize high-level planning and low-level execution separately, causing misalignment. The authors introduce Capability-Aligned Hierarchical Learning (CAHL), which jointly optimizes these policies using Reinforcement Learning with Value Regularization (RLVR).
✦ Why It Matters
Engineers can leverage CAHL to improve the performance of LLMs in applications requiring tool usage.
Key Takeaways
- Implement CAHL for better alignment in LLMs.
- Use RLVR to optimize planning and execution together.
- Test LLMs on diverse benchmarks for robust performance.
- Explore tool-augmented tasks to enhance application effectiveness.
Full Summary
Tool learning allows large language models (LLMs) to utilize external tools for task completion, but previous methods often treat high-level planning and low-level execution as separate processes. This separation can lead to misalignment, where the planner's goals do not effectively guide the executor's actions.
To address this, Capability-Aligned Hierarchical Learning (CAHL) was developed, which employs Reinforcement Learning with Value Regularization (RLVR) to optimize both the high-level and low-level policies simultaneously. The methodology was tested on constrained benchmarks like API-Bank and BFCL, as well as an open-ended environment called Bamboogle.
Results indicated that CAHL outperformed previous methods, demonstrating improved alignment and task execution efficiency. This advancement suggests that integrating planning and execution can enhance LLM capabilities in real-world applications, making them more effective in tool-augmented tasks.