Roy-DOU

WebCoder-27B

README

License: apache-2.0

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

Table with columns: Model, Vision2Web Avg, Webdev-Bench RS, Webdev-Bench Avg, Webdev-Bench DSR
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.

bash

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

bibtex

@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}}

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Model Details

Model Provider

Roy-DOU

Model Tree

Base

Qwen/Qwen3.8-27B

Fine-tuned

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality