WebAgent Project: Paving the Way for Intelligent Information Exploration
In today’s digital age, information is growing at an exponential rate. The challenge lies in how to efficiently access and utilize this vast amount of information. Alibaba Group’s Tongyi Lab has introduced the WebAgent project, aiming to leverage advanced large – model technology to assist users in autonomously searching for information within the complex online environment, thereby enabling intelligent information exploration.
An Overview of the WebAgent Project
The WebAgent project, developed by Alibaba Group’s Tongyi Lab, primarily consists of two core components: WebDancer and WebWalker. Together, these components form a powerful online information exploration system capable of simulating human browsing and information – seeking behaviors.
WebDancer: A Pioneer in Autonomous Information Exploration
WebDancer is a model dedicated to autonomous information exploration. By imitating human browsing and searching behaviors on the web, it achieves autonomous information acquisition and processing. This model is designed to address the challenges users face in the information – explosive era, that is, how to quickly and accurately locate the required information.
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Core Advantages: Utilizing the ReAct framework, WebDancer undergoes a four – stage training process, including web data construction, trajectory sampling, supervised fine – tuning, and reinforcement learning, enabling it to independently acquire web information exploration skills. - ▸
Performance: On the GAIA and WebWalkerQA benchmarks, WebDancer achieved pass rates of 61.1% and 54.6%, respectively, demonstrating its powerful information exploration capabilities.
WebWalker: A Benchmark for Web Browsing
WebWalker serves as a benchmark for assessing the performance of large language models (LLMs) in web browsing. It establishes a multi – agent framework to assist researchers in evaluating and enhancing models’ web information exploration abilities.
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Core Value: WebWalker offers a standardized testing environment, enabling researchers to evaluate and compare different models’ web browsing performance. - ▸
Academic Recognition: WebWalker has been accepted by the ACL 2025 main conference, underscoring its influence and recognition in the academic community.
Technical Highlights of WebDancer
The technical highlights of WebDancer lie in its innovative four – stage training paradigm and data – driven approach.
Four – Stage Training Paradigm
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Web Data Construction: By collecting a large amount of real – world web browsing data, the model is provided with foundational learning materials. -
Trajectory Sampling: Effective browsing trajectories are extracted from the constructed data to help the model comprehend web – browsing patterns. -
Supervised Fine – Tuning (SFT): Trajectory – level supervised fine – tuning enables the model to effectively initiate and learn basic browsing skills. -
Reinforcement Learning (RL): Further improvement of the model’s generalization ability through reinforcement learning allows it to adapt to various complex online environments.
Data – Driven Approach
WebDancer’s data – driven approach combines trajectory – level supervised fine – tuning and reinforcement learning (DAPO), offering a scalable pipeline for training intelligent agent systems.
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Trajectory – Level Supervised Fine – Tuning: By analyzing users’ web – browsing trajectories, the model learns effective browsing strategies. - ▸
Reinforcement Learning (DAPO): Through a system of rewards and punishments, the model continuously optimizes its browsing behavior, enhancing the efficiency of information acquisition.
How to Use WebDancer
Using WebDancer is quite straightforward. Here are the basic deployment and operation guidelines:
Environment Setup
First and foremost, you need to set up a suitable development environment:
conda create -n webdancer python = 3.12
pip install -r requirements.txt
Model Deployment
Download the WebDancer model and deploy it using the provided scripts:
cd scripts
bash deploy_model.sh WebDancer_PATH
Ensure that you replace WebDancer_PATH with the actual path of the downloaded model.
Running the Demo
After modifying the following API keys, launch the demo using Gradio:
cd scripts
bash run_demo.sh
The API keys that need modification include:
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GOOGLE_SEARCH_KEY - ▸
JINA_API_KEY - ▸
DASHSCOPE_API_KEY
Application Scenarios of WebDancer
WebDancer is capable of performing long – horizon tasks involving multiple steps and complex reasoning, such as web traversal, information seeking, and question answering.
WebWalkerQA Demo
In the WebWalkerQA demo, WebDancer demonstrates its ability to answer complex questions.
GAIA Demo
The GAIA demo further proves WebDancer’s efficiency and accuracy in handling large – scale and complex tasks.
Daily Use
In daily use, WebDancer can help users quickly find the information they need, thereby improving work efficiency.
Frequently Asked Questions (FAQs)
Q: What distinguishes WebDancer from WebWalker?
A: WebDancer focuses on autonomous information exploration, whereas WebWalker is a benchmark for evaluating the web – browsing performance of large language models. The two complement each other within the WebAgent project.
Q: How can I obtain the WebDancer model?
A: You can download the WebDancer model from HuggingFace.
Q: What API keys are required to run the demo?
A: To run the demo, you need to modify the following three API keys: GOOGLE_SEARCH_KEY, JINA_API_KEY, and DASHSCOPE_API_KEY.
Q: Which programming languages does WebDancer support?
A: WebDancer is primarily developed and deployed using Python.
**Q: How does WebDancer perform?
A: On the GAIA and WebWalkerQA benchmarks, WebDancer achieved pass rates of 61.1% and 54.6%, respectively, highlighting its superior performance.
Conclusion
The WebAgent project, through WebDancer and WebWalker, empowers users with robust online information exploration capabilities. Whether for academic research or daily use, this project reveals immense potential and value. If you’re interested in online information exploration, consider trying out WebDancer and experience the convenience and efficiency it brings.
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For further details, visit the HuggingFace page and the WebDancer-32B page, or read the related papers WebDancer and WebWalker.
Should you encounter any issues during usage, feel free to contact Jialong Wu, the project contributor, via jialongwu@alibaba – inc.com or jialongwu@seu.edu.cn.