BrowserAgent: Building Web Agents with Human-Inspired Web Browsing
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Authors: name: Zhengbo Zhang, name: Zhiheng Lyu, name: Junhao Gong, name: Hongzhu Yi, name: Xinming Wang, name: Yuxuan Zhou, name: Jiabing Yang, name: Ping Nie, name: Yan Huang, name: Wenhu Chen
Organization: TIGER-Lab

Efficiently solving real-world problems with LLMs increasingly hinges on
their ability to interact with dynamic web environments and autonomously
acquire external information. While recent research like Search-R1 and
WebDancer demonstrates strong performance in solving web tasks, they heavily
rely on additional tools to convert the interactive web environment into static
text content. This is in contrast to human browsing behaviors, which involve
diverse interactions with the browser, such as scrolling, clicking, and typing.
In this paper, we propose BrowserAgent, a more interactive agent that solves
complex tasks through human-inspired browser actions. BrowserAgent operates
directly on raw web pages via Playwright through a set of predefined browser
actions. We adopt a two-stage training (Supervised Fine-Tuning (SFT) and
Rejection Fine-Tuning (RFT)) to improve the model's generalization abilities.
Despite using significantly less training data than Search-R1, BrowserAgent
achieves more competitive results across different Open-QA tasks. Additionally,
we introduce an explicit memory mechanism to store key conclusions across
steps, further enhancing the model's reasoning capabilities for long-horizon
tasks. Notably, BrowserAgent-7B can achieve around 20\% improvement over
Search-R1 on multi-hop QA tasks like HotpotQA, 2Wiki, and Bamboogle. These
results indicate that BrowserAgent can serve as a more advanced framework for
more interactive and scalable web agents.