Adapting Web Agents with Synthetic Supervision

2025-11-08

AI Summary

Web agents exhibit poor adaptability to novel websites, while real-world task data is scarce and existing synthetic data suffers from hallucination, redundancy, and misaligned execution trajectories. Method: We propose a two-stage refinement framework that jointly optimizes synthetic task validity and trajectory fidelity. Our approach integrates webpage-element-classification-guided task generation, runtime task correction, and global-context-aware trajectory refinement to construct high-quality synthetic supervision signals, followed by fine-tuning of open-source web agents. Contribution/Results: Experiments demonstrate substantial improvements in task success rates across multiple unseen websites, outperforming state-of-the-art synthetic data methods. Our framework establishes a scalable, high-fidelity paradigm for synthetic data construction, enhancing web agent generalization under low-resource conditions.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Multiagent Systems: Adversarial Agents
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Web agents struggle to adapt to new websites due to the scarcity of environment specific tasks and demonstrations. Recent works have explored synthetic data generation to address this challenge, however, they suffer from data quality issues where synthesized tasks contain hallucinations that cannot be executed, and collected trajectories are noisy with redundant or misaligned actions. In this paper, we propose SynthAgent, a fully synthetic supervision framework that aims at improving synthetic data quality via dual refinement of both tasks and trajectories. Our approach begins by synthesizing diverse tasks through categorized exploration of web elements, ensuring efficient coverage of the target environment. During trajectory collection, we refine tasks when conflicts with actual observations are detected, mitigating hallucinations while maintaining task consistency. After collection, we conduct trajectory refinement with a global context to mitigate potential noise or misalignments. Finally, we fine-tune open-source web agents on the refined synthetic data to adapt them to the target environment. Experimental results demonstrate that SynthAgent outperforms existing synthetic data methods, validating the importance of high-quality synthetic supervision. The code will be publicly available at https://github.com/aiming-lab/SynthAgent.

Problem

• Web agents struggle to adapt to new websites due to data scarcity
• Synthetic data generation suffers from hallucinations and noisy trajectories
• SynthAgent refines synthetic tasks and trajectories to improve data quality

Innovation

• Synthesizing diverse tasks through categorized web exploration
• Refining tasks when conflicts with observations are detected
• Conducting trajectory refinement with global context post-collection