arXiv:2604.20398  (cs)
 



 

  Authors:  Juyong Jiang ,  Chenglin Cai ,  Chansung Park ,  Jiasi Shen ,  Sunghun Kim ,  Jianguo Li ,  Yue Wang               View a PDF of the paper titled WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning, by Juyong Jiang and 6 other authors 
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         Abstract: While Large Language Models (LLMs) excel at function-level code generation, project-level tasks such as generating functional and visually aesthetic multi-page websites remain highly challenging. Existing works are often limited to single-page static websites, while agentic frameworks typically rely on multi-turn execution with proprietary models, leading to substantial token costs, high latency, and brittle integration. Training a small LLM end-to-end with reinforcement learning (RL) is a promising alternative, yet it faces a critical bottleneck in designing reliable and computationally feasible rewards for website generation. Unlike single-file coding tasks that can be verified by unit tests, website generation requires evaluating inherently subjective aesthetics, cross-page interactions, and functional correctness. To this end, we propose WebGen-R1, an end-to-end RL framework tailored for project-level website generation. We first introduce a scaffold-driven structured generation paradigm that constrains the large open-ended action space and preserves architectural integrity. We then design a novel cascaded multimodal reward that seamlessly couples structural guarantees with execution-grounded functional feedback and vision-based aesthetic supervision. Extensive experiments demonstrate that our WebGen-R1 substantially transforms a 7B base model from generating nearly nonfunctional websites into producing deployable, aesthetically aligned multi-page websites. Remarkably, our WebGen-R1 not only consistently outperforms heavily scaled open-source models (up to 72B), but also rivals the state-of-the-art DeepSeek-R1 (671B) in functional success, while substantially exceeding it in valid rendering and aesthetic alignment. These results position WebGen-R1 as a viable path for scaling small open models from function-level code generation to project-level web application generation.
 

 
 
    
       Subjects: 
       
         Computation and Language (cs.CL) ; Machine Learning (cs.LG); Software Engineering (cs.SE) 
      
       Cite as: 
         arXiv:2604.20398  [cs.CL]  
     
     
         
       (or  
           arXiv:2604.20398v1  [cs.CL]  for this version)
       
     
     
         
                      https://doi.org/10.48550/arXiv.2604.20398  
           
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