ToolMaster is a framework that shifts tool learning from static imitation to a
trial-and-execution paradigm, enabling Large Language Models (LLMs) to actively master tools. It was introduced in the paper
Teaching LLMs to Learn Tool Trialing and Execution through Environment Interaction.
Existing tool-use paradigms primarily rely on memorizing static solution paths during training, which limits the ability of LLMs to generalize to new or evolving tools. ToolMaster addresses this by training agents to:
By leveraging Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) using Group Relative Policy Optimization (GRPO), ToolMaster empowers agents to dynamically adapt to unfamiliar tools, significantly enhancing generalization and robustness.
For detailed instructions on environment setup, data preparation, and evaluation (on benchmarks like ToolHop, TMDB, and StableToolBench), please refer to the
official GitHub repository.
1@article{gao2025teaching,
2 title={Teaching LLMs to Learn Tool Trialing and Execution through Environment Interaction},
3 author={Gao, Xingjie and others},
4 journal={arXiv preprint arXiv:2601.12762},
5 year={2025}
6}
