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

TL;DR

This paper introduces a multi-modal agent tuning approach that automatically generates training data for vision-language models, significantly improving their ability to reason about and utilize external tools in practical tasks.

Contribution

The paper presents a novel data synthesis pipeline and a tuning method for VLMs, enhancing multi-modal agent tool-usage reasoning with 20K synthesized task trajectories.

Findings

T3-Agent outperforms untrained VLMs by 20% on benchmarks.

The data synthesis pipeline improves tool-usage capabilities.

Enhanced VLMs show better reasoning in practical tasks.

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.