What is WebResearcher？
WebResearcher is an iterative deep research agent developed by Alibaba’s Tongyi Lab, part of the Tongyi DeepResearch family. Based on an innovative iterative deep research paradigm, it simulates the cognitive workflow of human experts, autonomously decomposing complex problems, coordinating the use of tools, and integrating discoveries into coherent, well-reasoned narratives. Compared to traditional research agents, WebResearcher processes research in multiple stages to avoid information overload and noise accumulation, ensuring sustained deep reasoning capabilities. It features an extensible data synthesis engine and a dedicated multi-stage training process, including rejection-based fine-tuning and verifiable-reward reinforcement learning, demonstrating exceptional performance in complex reasoning tasks.
Key Features of WebResearcher
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Autonomous Problem Decomposition: Breaks down complex research tasks into manageable sub-tasks.
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Tool Coordination: Calls various tools as needed, such as search engines and academic databases.
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Integration of Findings: Synthesizes retrieved information and tool outputs into coherent, well-reasoned narratives.
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Sustained Deep Reasoning: Uses an iterative process to maintain deep reasoning while avoiding information overload and noise accumulation.
Technical Principles of WebResearcher
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Iterative Research Process: Research is broken down into discrete rounds, each comprising “Think,” “Report,” and “Action.” The “Report” from each round serves as a central memory, integrating new findings into a coherent, high-density summary for the next round. This cyclic synthesis and reconstruction process prevents cognitive overload and noise contamination, enabling sustained deep reasoning.
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Extensible Data Synthesis Engine: Uses a multi-agent framework to automatically generate large-scale, high-quality, and complex reasoning task data through three stages: initial data generation, iterative complexity enhancement, and rigorous quality control.
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Training and Inference:
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Rejection-based Fine-Tuning (RFT): Fine-tunes on high-quality trajectories to ensure final answers align with ground truth, cultivating robust tool usage and knowledge-based reasoning.
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Reinforcement Learning with Verifiable Rewards (RLVR): Enhances multi-step logical reasoning abilities through reinforcement learning with verifiable rewards.
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Test-Time Scaling (TTS): During inference, multiple parallel reasoning paths are run, and a specialized fusion agent synthesizes the final answer from the last few steps of each path, improving performance.
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Project Links
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GitHub Repository: https://github.com/Alibaba-NLP/DeepResearch/tree/main/WebAgent/WebResearcher
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arXiv Paper: https://arxiv.org/pdf/2509.13309
Application Scenarios of WebResearcher
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Academic Research: Helps researchers quickly review literature, extract key information, and support complex academic projects, improving efficiency and quality.
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Market Analysis: Collects and analyzes market data to identify industry trends and consumer demands, providing enterprises with precise market insights for informed decision-making.
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Technology Development: Used for technology trend research and competitive analysis, helping developers stay on the cutting edge and accelerate technological iteration.
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Education and Tutoring: Integrates learning resources and provides knowledge explanations for students and educators, supporting teaching and learning processes.
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Healthcare: Assists medical professionals in disease research, drug development, and information gathering, providing data support and knowledge context for medical decision-making.
