Pushing the Limits of LLM Tool Calling via Experiential Knowledge Integration and Activation

The paper introduces the Knowledge-Augmented Tool Execution (KATE) framework, which enhances the performance of large language models (LLMs) in tool use by integrating experiential knowledge and modifying inference strategies. Key findings include that expanding the width of reasoning through parallel sampling significantly activates latent knowledge, while post-training with knowledge-augmented data and reinforcement learning yields superior results compared to traditional supervised fine-tuning. Experiments on BFCL-V3 and AppWorld show substantial improvements over existing baselines, underscoring the importance of effective knowledge integration for practitioners developing autonomous AI agents.