WebCoder-27B

Project page · Code · Webdev-Bench · Paper

WebCoder-27B is a general frontend development agent, trained from Qwen3.8-27B with artifact-grounded agentic reinforcement learning. It is the model released with the paper

WebCoder: Scaling General Frontend Development with Artifact-Grounded Agentic Reinforcement Learning Zijun Dou*, Zhen Yang*, Xijun Liu, Dongzhuoran Zhou, Aohan Zeng, Jie Tang *Core contributors.

Overview

Real-world frontend development requires an agent to compose reproduction, generation, modification and repair over an evolving application state. WebCoder assesses all of these activities through their effects on a shared executable artifact and on its satisfaction of the task requirements.

• Webdev-World — an executable environment that integrates code editing, application execution, browser inspection and on-demand skills, preserving project state across interactions.
• Artifact-grounded reward and credit assignment — an independent verifier evaluates the final application against the task requirements to give a terminal reward; changes in intermediate artifact progress redistribute the resulting rollout advantage across development segments.
• Long-horizon training — context compaction and message-tree sample construction support training on trajectories beyond a single context window, without repeated loss computation on shared generated history.

Results

Model	Vision2Web Avg	Webdev-Bench RS	Webdev-Bench Avg	Webdev-Bench DSR
Claude-Opus-4.8	62.5	65.1	73.7	100.0%
GPT-5.4	57.9	59.6	70.8	100.0%
Qwen3.8-27B (base)	41.1	64.8	72.8	95.0%
WebCoder-27B	62.4	72.8	79.8	100.0%

RS is requirement satisfaction and DSR the delivery success rate. The full table, with every baseline and every sub-metric, is in the paper and on the project page.

Usage

WebCoder-27B keeps the architecture (Qwen3_5ForConditionalGeneration), tokenizer, chat template and processor configuration of its base model, so it is served the same way as Qwen3.8-27B — for example with vLLM or SGLang. See the Qwen3.8-27B model card for serving commands and API examples.

vllm serve Roy-DOU/WebCoder-27B --tensor-parallel-size 4

generation_config.json sets temperature=1.0, top_p=0.95, top_k=20.

The model was trained as an agent: it acts through tools in a development environment over many turns, and is at its best in that setting rather than in single-turn chat.

License

Released under the Apache License 2.0, the license of the base model. See LICENSE.

Citation

@article{dou2026webcoder,
  title   = {WebCoder: Scaling General Frontend Development with Artifact-Grounded Agentic Reinforcement Learning},
  author  = {Dou, Zijun and Yang, Zhen and Liu, Xijun and Zhou, Dongzhuoran and Zeng, Aohan and Tang, Jie},
  journal = {arXiv preprint arXiv:2610.23104},
  year    = {2026}
}