Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage

• URL: http://arxiv.org/abs/2412.15606v2
• Date: Mon, 03 Feb 2025 12:56:55 GMT
• Title: Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage
• Authors: Zhi Gao, Bofei Zhang, Pengxiang Li, Xiaojian Ma, Tao Yuan, Yue Fan, Yuwei Wu, Yunde Jia, Song-Chun Zhu, Qing Li,
• Abstract summary: We propose a multi-modal agent tuning method that automatically generates multi-modal tool-usage data. To preserve the data quality, we prompt the GPT-4o mini model to generate queries, files, and trajectories. Evaluations show that the T3-Agent consistently achieves improvements on two popular VLMs.
• Score: 75.76940471949366
• License:
• Abstract: The advancement of large language models (LLMs) prompts the development of multi-modal agents, which are used as a controller to call external tools, providing a feasible way to solve practical tasks. In this paper, we propose a multi-modal agent tuning method that automatically generates multi-modal tool-usage data and tunes a vision-language model (VLM) as the controller for powerful tool-usage reasoning. To preserve the data quality, we prompt the GPT-4o mini model to generate queries, files, and trajectories, followed by query-file and trajectory verifiers. Based on the data synthesis pipeline, we collect the MM-Traj dataset that contains 20K tasks with trajectories of tool usage. Then, we develop the T3-Agent via \underline{T}rajectory \underline{T}uning on VLMs for \underline{T}ool usage using MM-Traj. Evaluations on the GTA and GAIA benchmarks show that the T3-Agent consistently achieves improvements on two popular VLMs: MiniCPM-V-8.5B and {Qwen2-VL-7B}, which outperforms untrained VLMs by $20\%$, showing the effectiveness of the proposed data synthesis pipeline, leading to high-quality data for tool-usage capabilities.

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