iTool: Reinforced Fine-Tuning with Dynamic Deficiency Calibration for Advanced Tool Use

Understand

Augmenting large language models (LLMs) with external tools is a promising approach to enhance their capabilities, especially for complex tasks.

• Synthesizing tool-use data through real-world simulations is an effective way to achieve this.
• However, our investigation reveals that training gains significantly decay as synthetic data increases.
• The model struggles to benefit from additional synthetic data, which fails to endow it with advanced tool-use capabilities in complex scenarios Moreover, we discovered that the above limitation usually manifests as a fragment deficiency (i.e., parameter errors) in response.