# InfiniteWeb: Scalable Web Environment Synthesis for GUI Agent Training

arXiv: https://arxiv.org/abs/2601.04126 · HTML: https://mlsanity.com/paper/2601.04126/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 20 of 20)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Mind2Web: Towards a Generalist Agent for the Web](https://mlsanity.com/paper/2306.06070/) (2023)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023)
- [Leveraging Procedural Generation to Benchmark Reinforcement Learning](https://mlsanity.com/paper/1912.01588/) (2019)
- [CodeT: Code Generation with Generated Tests](https://mlsanity.com/paper/2207.10397/) (2022)
- [Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration](https://mlsanity.com/paper/1802.08802/) (2018)
- [ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use](https://mlsanity.com/paper/2504.07981/) (2025)
- [An Illusion of Progress? Assessing the Current State of Web Agents](https://mlsanity.com/paper/2504.01382/) (2025)
- [OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis](https://mlsanity.com/paper/2412.19723/) (2024)
- [Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents](https://mlsanity.com/paper/2504.00906/) (2025)
- [Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering](https://mlsanity.com/paper/2403.03163/) (2024)
- [AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials](https://mlsanity.com/paper/2412.09605/) (2024)
- [WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch](https://mlsanity.com/paper/2505.03733/) (2025)
- [MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments](https://mlsanity.com/paper/2512.19432/) (2025)
- [MMBench-GUI: Hierarchical Multi-Platform Evaluation Framework for GUI Agents](https://mlsanity.com/paper/2507.19478/) (2025)
- [AgentSynth: Scalable Task Generation for Generalist Computer-Use Agents](https://mlsanity.com/paper/2506.14205/) (2025)
- [UI-E2I-Synth: Advancing GUI Grounding with Large-Scale Instruction Synthesis](https://mlsanity.com/paper/2504.11257/) (2025)

## Built on by (most cited first, 27 of 27)

- [Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence](https://mlsanity.com/paper/2604.18292/) (2026)
- [Qwen-AgentWorld: Language World Models for General Agents](https://mlsanity.com/paper/2606.24597/) (2026)
- [CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents](https://mlsanity.com/paper/2605.25624/) (2026)
- [AutoWebWorld: Synthesizing Infinite Verifiable Web Environments via Finite State Machines](https://mlsanity.com/paper/2602.14296/) (2026)
- [Recursive Synthesis for Long-Horizon Terminal Tasks](https://mlsanity.com/paper/2608.05466/) (2026)
- [Who&When Pro: Can LLMs Really Attribute Failures in AI Agents?](https://mlsanity.com/paper/2607.09996/) (2026)
- [MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research](https://mlsanity.com/paper/2605.26114/) (2026)
- [SPADE: Self-Play in Adaptive Synthetic Executable Environments](https://mlsanity.com/paper/2608.19197/) (2026)
- [UI-Voyager: A Self-Evolving GUI Agent Learning via Failed Experience](https://mlsanity.com/paper/2603.24533/) (2026)
- [OpenComputer: Verifiable Software Worlds for Computer-Use Agents](https://mlsanity.com/paper/2605.19769/) (2026)
- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [Learning to Build the Environment: Self-Evolving Reasoning RL via Verifiable Environment Synthesis](https://mlsanity.com/paper/2605.14392/) (2026)
- [Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application](https://mlsanity.com/paper/2606.12191/) (2026)
- [Qwen-CUA: Native Computer Use for (almost) Everything](https://mlsanity.com/paper/2608.02352/) (2026)
- [SWE-AGILE: A Software Agent Framework for Efficiently Managing Dynamic Reasoning Context](https://mlsanity.com/paper/2604.11716/) (2026)
- [PhoneBuddy: Training Open Models for Agentic Phone Use](https://mlsanity.com/paper/2606.23049/) (2026)
- [Scalable Environments Drive Generalizable Agents](https://mlsanity.com/paper/2605.18181/) (2026)
- [Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale](https://mlsanity.com/paper/2607.28074/) (2026)
- [CUA-Universe: A Scalable and Dynamic Environment for Hybrid GUI+CLI Agents](https://mlsanity.com/paper/2609.05374/) (2026)
- [PhoneWorld: From Real-App Trajectories to Dynamic and Verifiable Environments for Phone-Use Agents](https://mlsanity.com/paper/2605.29486/) (2026)
- [UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations](https://mlsanity.com/paper/2608.15930/) (2026)
- [RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use Agents](https://mlsanity.com/paper/2609.22000/) (2026)
- [From Trajectories to Grounded Preferences: Process Preference Synthesis via Interaction Element Graphs for Web PRMs](https://mlsanity.com/paper/2609.32351/) (2026)
- [CompoWorld: Compositional Environment Scaling for General Agents](https://mlsanity.com/paper/2609.33665/) (2026)
- [WEFT: Scaling Tool-Use Post-Training for General-Purpose Agents](https://mlsanity.com/paper/2609.36887/) (2026)
- [AutoGUIWorld: Image Generators as Visual World Models for GUI Agent](https://mlsanity.com/paper/2610.01215/) (2026)
- [AutoEnvScaling: Automating the Data Flywheel with Terminal Agents](https://mlsanity.com/paper/2610.14163/) (2026)

## Abstract (arXiv)

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.