MetaToolAgent: Towards Generalizable Tool Usage in LLMs through Meta-Learning

2026-01-19

AI Summary

This work addresses the limited generalization of large language models (LLMs) when encountering unseen tools and their difficulty in flexibly coordinating diverse tools to accomplish complex tasks. To tackle this challenge, the authors construct a comprehensive multi-tool dataset spanning seven domains and propose MetaToolAgent, a novel framework that introduces meta-learning into tool-use scenarios for the first time. By leveraging meta-learning, MetaToolAgent enhances the model’s ability to rapidly adapt to and efficiently invoke new tools. Experimental results demonstrate that the proposed method significantly outperforms existing baselines, exhibiting strong generalization on unseen tools, supporting dynamic tool coordination, and enabling seamless system extensibility—thereby validating its flexibility and scalability in real-world applications.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Multiagent Learning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Tool learning is increasingly important for large language models (LLMs) to effectively coordinate and utilize a diverse set of tools in order to solve complex real-world tasks. By selecting and integrating appropriate tools, LLMs extend their capabilities beyond pure language understanding to perform specialized functions. However, existing methods for tool selection often focus on limited tool sets and struggle to generalize to novel tools encountered in practical deployments. To address these challenges, we introduce a comprehensive dataset spanning 7 domains, containing 155 tools and 9,377 question-answer pairs, which simulates realistic integration scenarios. Additionally, we propose MetaToolAgent (MTA), a meta-learning approach designed to improve cross-tool generalization. Experimental results show that MTA significantly outperforms baseline methods on unseen tools, demonstrating its promise for building flexible and scalable systems that require dynamic tool coordination.

Problem

• tool learning
• generalization
• large language models
• novel tools
• tool selection

Innovation

• meta-learning
• tool learning
• generalization
• large language models
• cross-tool coordination