WebGrader: Training LLMs for Web Development with Self-Evolving Programmatic Grader
Large language models increasingly generate complete websites from natural-language descriptions, and reinforcement learning has become a central approach to closing their remaining functional gap. This training regime is bottlenecked by reward design. Hand-authored browser scripts are executable yet costly to write for open-ended requirements, while VLM and GUI-agent graders scale but may issue verdicts before observing the decisive state. We propose WebGrader, a self-evolving programmatic grader that autonomously derives the required interaction flows from each website request, represents each flow as an executable Flow Contract, and uses its execution outcome as an RL reward. WebGrader...
| Authors | Boshui Chen, Huiping Liu, Shaolei Zhang |
|---|---|
| Primary category | cs.AI |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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