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RILA is an execution‑driven agent that integrates browser rendering into the generation loop for interactive web development. It uses an Action Interaction Verification module to replay reference interactions on generated pages, collecting execution‑aware observations, and an Execution‑aware Rendering Score to jointly assess interaction correctness and visual fidelity during iterative optimization. A data synthesis pipeline further augments training data, enabling RILA to significantly improve interaction and visual quality across foundation models, even outperforming larger one‑shot generators.
The paper introduces WebLoop, an execution‑grounded framework that jointly trains generation, critique, and refinement within a shared policy for functional web generation. WebLoop trains an execution‑free Critic using requirement‑level discriminability and downstream helpfulness signals, then incorporates consequence‑aware credit to improve diagnosis and repair. Experiments with Qwen3.5‑9B show significant gains on WebRise and WebGen‑Bench, with benefits that transfer to larger models and multimodal settings.
arXiv:2608. 06474v1 Announce Type: new Abstract: Large language models increasingly generate complete websites from natural-language descriptions, and reinforcement learning has become a central approach to closing their remaining functional gap.
arXiv:2606. 00154v1 Announce Type: cross Abstract: Recent advancements in multimodal large language models (MLLMs) have achieved remarkable progress in multimodal reasoning and code generation, catalyzing a new paradigm for front-end development.
arXiv:2608.27906v2 Announce Type: replace Abstract: Interactive web application generation requires models to produce usable HTML, CSS, and JavaScript applications from natural language requests. Unl...
arXiv:2610.26826v2 Announce Type: replace-cross Abstract: Beyond basic tool use and coding skills, effectively incorporating visual references into agentic generation is equally essential for applyin...