Effective agent tuning in large language models is crucial for bridging the gap between open-sourced LLMs and API-based models.
Agent-FLAN proposes effective fine-tuning methods for integrating agent abilities into large language models, outperforming prior works by 3.5% across various agent evaluation datasets.