Visual Agentic Reinforcement Fine-Tuning
Ziyu Liu, Yuhang Zang, Yushan Zou, Zijian Liang, Xiaoyi Dong, Yuhang Cao, Haodong Duan, Dahua Lin, Jiaqi Wang
2025-05-21
Summary
This paper talks about Visual Agentic Reinforcement Fine-Tuning, a new way to train AI models so they can better understand and work with both pictures and words, especially when changing images or reasoning about things on the web.
What's the problem?
The problem is that even advanced AI models struggle to handle tasks that require understanding both images and text at the same time, especially when they need to change images or make decisions based on what they see and read online.
What's the solution?
To solve this, the researchers used a special training method that rewards the AI for making smart choices when working with images and text together. This approach helps the AI become more flexible and skilled at handling complicated tasks that mix visuals and language.
Why it matters?
This matters because it means AI can become much better at helping with things like editing photos, searching the internet, or answering questions that involve both pictures and words, making technology more helpful and interactive for everyone.
Abstract
Visual Agentic Reinforcement Fine-Tuning enhances Large Vision-Language Models for flexible image manipulation and web-based reasoning, outperforming existing models on multi-modal benchmarks.