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

Jingyu Xiao, Dasen Dai, Yuxuan Wan, Jen-tse Huang, Yintong Huo, Hongyu Zhang, Michael R. Lyu · 2026 · arXiv

DOI: 10.48550/arXiv.2610.28610 · open-access PDF

French

Les pages web interactives sous-tendent les applications modernes, mais la reproduction à la fois de leur apparence visuelle et de leur comportement en plusieurs étapes reste un défi pour les modèles de grands langages multimodaux (MLLM). Les données de formation évolutives doivent capturer les actions des utilisateurs, les transitions d'état et les résultats attendus, tandis que le code de la page web du monde réel est souvent mêlé à des scripts irréguliers et à des dépendances externes.En outre, la vérification existante basée sur l'agent GUI fournit un retour fonctionnel lent et potentiellement instable.

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 generat

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