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arXiv AI By Zijun Dou, Zhen Yang, Xijun Liu, Dongzhuoran Zhou, Aohan Zeng, Jie Tang

WebCoder: Scaling General Frontend Development with Artifact-Grounded Agentic Reinforcement Learning

Read the original on arXiv AI →

WebCoder is a general frontend development agent that uses artifact‑grounded agentic reinforcement learning to learn how to compose, generate, modify, and repair code over an evolving application state. It evaluates development actions by their impact on a shared executable artifact and a verifier that provides terminal rewards, while intermediate progress redistributes rollout advantage across development segments. The system includes Webdev‑World, an executable environment that integrates code editing, execution, browser inspection, and on‑demand skills, and Webdev‑Bench, a benchmark for composed development tasks, where WebCoder‑27B outperforms several large language models on requirement satisfaction and visual/functional scores.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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