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

Query: I want to buy a PS5 for each child in the photo. Thought: Use the `facedetection` tool to detect Thought: First analyze the image 1 to find the number human faces in the two images. Faces in Image 1: 4 bounding boxes Thought: There are 4 children in total. The price of Price of PS5: $479.99 a PS5 is approximately $500, so the cost is 4* 500. Thought: Using the price of $479.99 for each console. Query: The men in the picture want to buy one NVIDIA GeForce RTX 4070 SUPER each. According to the price in January, how many dollars will they need to spend in total? Observation: This image does not provide any price. On January 8, 2024, Nvidia released the RTX Thought: I cannot obtain useful information. I 4070 SUPER at $599, think the price is about $1800 for three men. Thought: The price is $599. Our agent chooses more precise tools based on the given files and intermediate observations. 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 Trajectory Tuning on VLMs for Tool 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 Integrating external tools to solve diverse multi-modal tasks is a promising research direction towards multi-modal agents (Surís et al., 2023; Gupta & Kembhavi, 2023; Gao et al., 2024; Yuan et al., 2024; Zhong et al., 2023). Existing agents usually use a large language model (LLM) as the controller that generates plans via prompt engineering to call tools, achieving impressive performance in multiple domains, such as image editing (Wu et al., 2023), robotic manipulation (ichter et al., 2023), question answering (Shen et al., 2024), video understanding (Fan et al., 2024), and desktop APPs (Trivedi et al., 2024). Despite their success, prompt engineering faces limited reasoning abilities for tool usage in tackling practical tasks, as shown in Figure 1.