InfiniteWeb: Scalable Web Environment Synthesis for GUI Agent Training

2026-01-07

arXiv.org

AI Summary

This work addresses the challenge of training GUI agents, which is hindered by the scarcity of realistic and interactive web environments. To overcome this limitation, the authors propose a task-driven automated approach that synthesizes large-scale, functionally complete, and interlinked web environments under a unified specification. Diversity is ensured by leveraging website seeds and reference design mockups. Additionally, the framework includes a verifiable task evaluator that provides dense reward signals to facilitate reinforcement learning. The generated environments exhibit higher realism than those produced by existing commercial coding agents, and GUI agents trained within them achieve significantly improved performance on the OSWorld and Online-Mind2Web benchmarks.

Technology Category

• Multiagent Systems: Adversarial Agents
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

GUI agents that interact with graphical interfaces on behalf of users represent a promising direction for practical AI assistants. However, training such agents is hindered by the scarcity of suitable environments. We present InfiniteWeb, a system that automatically generates functional web environments at scale for GUI agent training. While LLMs perform well on generating a single webpage, building a realistic and functional website with many interconnected pages faces challenges. We address these challenges through unified specification, task-centric test-driven development, and a combination of website seed with reference design image to ensure diversity. Our system also generates verifiable task evaluators enabling dense reward signals for reinforcement learning. Experiments show that InfiniteWeb surpasses commercial coding agents at realistic website construction, and GUI agents trained on our generated environments achieve significant performance improvements on OSWorld and Online-Mind2Web, demonstrating the effectiveness of proposed system.

Problem

• GUI agent training
• web environment synthesis
• scalable environment generation
• functional website generation
• training data scarcity

Innovation

• web environment synthesis
• GUI agent training
• test-driven development
• LLM-based generation
• dense reward signals