BrowserAgent: Building Web Agents with Human-Inspired Web Browsing
  Actions
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.
68edce77de1fee572713a943
Zhengbo Zhang
68edce77de1fee572713a944
Zhiheng Lyu
68edce77de1fee572713a945
Junhao Gong
68edce77de1fee572713a946
Hongzhu Yi
68edce77de1fee572713a947
Xinming Wang
68edce77de1fee572713a948
Yuxuan Zhou
68edce77de1fee572713a949
Jiabing Yang
68edce77de1fee572713a94a
Ping Nie
claimed_verified
2025-10-15T03:13:54.894Z
65358802a920f38780b3248a
/avatars/9415510b598079973c2b0436ad12db9c.svg
Ping Nie
user
pingnieuk
68edce77de1fee572713a94b
Yan Huang
68edce77de1fee572713a94c
Wenhu Chen
BrowserAgent: Building Web Agents with Human-Inspired Web Browsing
  Actions
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.
BrowserAgent, an interactive web agent using human-like browser actions and a two-stage training process, achieves competitive results in Open-QA tasks with less training data and improved reasoning for multi-hop QA.
LLMs BrowserAgent Playwright Supervised Fine-Tuning Rejection Fine-Tuning explicit memory mechanism multi-hop QA HotpotQA 2Wiki Bamboogle