# Artifact-Grounded Agentic Reinforcement Learning

WebCoder-27B, trained during an internship at Zhipu AI, keeps reproduction, generation, modification and repair on one evolving project state and scores reinforcement learning by how well the executable application meets task requirements. On the new Webdev-Bench its requirement satisfaction rises from 64.8 for the Qwen3.8-27B base model to 72.8, above Claude-Opus-4.8 at 65.1; on Vision2Web its average visual and functional score rises from 41.1 to 62.4, on par with Claude-Opus-4.8 at 62.5.

WebCoder-27B, trained during an internship at Zhipu AI, keeps reproduction, generation, modification and repair on one evolving project state and scores reinforcement learning by how well the executable application meets task requirements. On the new Webdev-Bench its requirement satisfaction rises from 64.8 for the Qwen3.8-27B base model to 72.8, above Claude-Opus-4.8 at 65.1; on Vision2Web its average visual and functional score rises from 41.1 to 62.4, on par with Claude-Opus-4.8 at 62.5.