Summary of Multi-modal Agent Tuning: Building a Vlm-driven Agent For Efficient Tool Usage, by Zhi Gao et al.
Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage
by Zhi Gao, Bofei Zhang, Pengxiang Li, Xiaojian Ma, Tao Yuan, Yue Fan, Yuwei Wu, Yunde Jia, Song-Chun Zhu, Qing Li
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed multi-modal agent tuning method generates multi-modal tool-usage data and tunes a vision-language model as the controller for powerful tool-usage reasoning. The method prompts GPT-4o mini models to generate queries, files, and trajectories, which are verified through query-file and trajectory verifiers. The dataset MM-Traj contains 20K tasks with trajectories of tool usage, and the T3-Agent outperforms untrained VLMs by 20% on GTA and GAIA benchmarks using MiniCPM-V-8.5B and Qwen2-VL-7B. This approach leads to high-quality data for tool-usage capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a way to make computers better at doing tasks that involve using tools, like drawing or writing code. They create a system that can learn from examples of how to use different tools and then apply those skills to new situations. The system is tested on two popular computer programs and shows significant improvements over the untrained versions. |
Keywords
» Artificial intelligence » Gpt » Language model » Multi modal
