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InterWebCoder: Learning Interactive Webpage Generation from Executable Tests via Reinforcement Learning

Jingyu Xiao, D. Z. Dai, Yuxuan Wan, Jen-tse Huang, Yintong Huo, Hongyu Zhang, Michael Rung-Tsong Lyu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28610 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

互动的外部验证网页仍然是现代应用程序的基础,但重现其视觉外观和多步行为仍然是多式大语言模型(MLLMs)的挑战。可扩展的培训数据必须捕捉用户行动,状态过渡和预期结果,而真实的网页代码往往被无关紧要的脚本和外部依赖所困扰。此外,现有的基于GUI的验证提供了缓慢和潜在的不稳定的功能反馈。为了应对这些挑战,我们提出了InterWebCoder,一个有效的框架,用于互动的网页代码生成。InterWebCoder引入了一个测试规格引导和执行验证的数据合成管道,该管道记录了真实世界的交互轨迹54并合成了具有自定义功能的网页代码,并

Machine translation (Meta M2M100); the English original follows.

Abstract

Interactive webpages underpin modern applications, but reproducing both their visual appearance and multi-step behavior remains challenging for multimodal large language models (MLLMs). Scalable training data must capture user actions, state transitions, and expected outcomes, while real-world webpage code is often entangled with irrelevant scripts and external dependencies. Moreover, existing GUI-agent-based verification provides slow and potentially unstable functional feedback. To address these challenges, we propose InterWebCoder, an efficient framework for interactive webpage code generation. InterWebCoder introduces a Test-Specification-Guided and Execution-Verified Data Synthesis pipeline that records real-world interaction trajectories and synthesizes self-contained webpage code with executable test scripts. A failureattributed repair mechanism uses runtime evidence to locate and iteratively fix errors in either the webpage code or test script. We then combine test-ID-weighted supervised fine-tuning with group-relative policy optimization, directly optimizing functional correctness and visual fidelity using test pass rates and state-level visual similarity. This replaces costly GUI-agent verification with fast, reproducible executable feedback. Our pipeline produces 1,736 verified samples from 2,058 tasks, achieving an 84.35% synthesis success rate. On IWR-Bench and Vision2Web, InterWebCoder achieves final scores of 48.54 and 48.42 with a 9B backbone, outperforming 30B-level models and Gemini-3.7-Flash while remaining competitive with Claude-Sonnet-5. It reduces reward-computation latency by over 97%, eliminates external MLLM verification costs, and accelerates end-to-end training by up to 1.4x.

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Topics: Computer science, Executable, Web page, Correctness, Scripting language, Code (set theory)

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