Visual Agentic Reinforcement Fine-Tuning
A key trend in Large Reasoning Models (e.g., OpenAI's o3) is the native
agentic ability to use external tools such as web browsers for searching and
writing/executing code for image manipulation to think with images. In the
open-source research community, while significant progress has been made in
language-only agentic abilities such as function calling and tool integration,
the development of multi-modal agentic capabilities that involve truly thinking
with images, and their corresponding benchmarks, are still less explored. This
work highlights the effectiveness of Visual Agentic Reinforcement Fine-Tuning
(Visual-ARFT) for enabling flexible and adaptive reasoning abilities for Large
Vision-Language Models (LVLMs). With Visual-ARFT, open-source LVLMs gain the
ability to browse websites for real-time information updates and write code to
manipulate and analyze input images through cropping, rotation, and other image
processing techniques. We also present a Multi-modal Agentic Tool Bench (MAT)
with two settings (MAT-Search and MAT-Coding) designed to evaluate LVLMs'
agentic search and coding abilities. Our experimental results demonstrate that
Visual-ARFT outperforms its baseline by +18.6% F1 / +13.0% EM on MAT-Coding and
+10.3% F1 / +8.7% EM on MAT-Search, ultimately surpassing GPT-4o. Visual-ARFT
also achieves +29.3 F1% / +25.9% EM gains on existing multi-hop QA benchmarks
such as 2Wiki and HotpotQA, demonstrating strong generalization capabilities.
Our findings suggest that Visual-ARFT offers a promising path toward building
robust and generalizable multimodal agents.
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Ziyu Liu
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2025-05-21T10:08:39.517Z
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ziyuliu
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ziyuliu
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Yuhang Zang
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2025-05-21T08:39:42.962Z
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Yuhang Zang
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yuhangzang
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Yushan Zou
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2025-05-21T10:08:45.972Z
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yushan zou
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zyshan
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Zijian Liang
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2025-05-21T10:08:51.938Z
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Zijian Liang
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steins1096
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Xiaoyi Dong
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2025-05-21T10:09:00.912Z
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Xiaoyi Dong
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sweetFruit
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Yuhang Cao
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2025-05-21T10:09:22.645Z
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Cao Yuhang
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yhcao
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Haodong Duan
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2025-05-21T10:09:31.442Z
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HAODONG DUAN
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KennyUTC
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Dahua Lin
admin_assigned
2025-05-21T10:09:37.103Z
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Dahua Lin
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lindahua
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Jiaqi Wang
claimed_verified
2025-05-21T08:39:45.391Z
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Jiaqi Wang
user
myownskyW7
Visual Agentic Reinforcement Fine-Tuning
A key trend in Large Reasoning Models (e.g., OpenAI's o3) is the native
agentic ability to use external tools such as web browsers for searching and
writing/executing code for image manipulation to think with images. In the
open-source research community, while significant progress has been made in
language-only agentic abilities such as function calling and tool integration,
the development of multi-modal agentic capabilities that involve truly thinking
with images, and their corresponding benchmarks, are still less explored. This
work highlights the effectiveness of Visual Agentic Reinforcement Fine-Tuning
(Visual-ARFT) for enabling flexible and adaptive reasoning abilities for Large
Vision-Language Models (LVLMs). With Visual-ARFT, open-source LVLMs gain the
ability to browse websites for real-time information updates and write code to
manipulate and analyze input images through cropping, rotation, and other image
processing techniques. We also present a Multi-modal Agentic Tool Bench (MAT)
with two settings (MAT-Search and MAT-Coding) designed to evaluate LVLMs'
agentic search and coding abilities. Our experimental results demonstrate that
Visual-ARFT outperforms its baseline by +18.6% F1 / +13.0% EM on MAT-Coding and
+10.3% F1 / +8.7% EM on MAT-Search, ultimately surpassing GPT-4o. Visual-ARFT
also achieves +29.3 F1% / +25.9% EM gains on existing multi-hop QA benchmarks
such as 2Wiki and HotpotQA, demonstrating strong generalization capabilities.
Our findings suggest that Visual-ARFT offers a promising path toward building
robust and generalizable multimodal agents.
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.
Visual Agentic Reinforcement Fine-Tuning Large Vision-Language Models Multi-modal Agentic Tool Bench MAT-Search MAT-Coding multi-hop QA benchmarks