ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web Coding

• URL: http://arxiv.org/abs/2510.11498v1
• Date: Mon, 13 Oct 2025 15:05:50 GMT
• Title: ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web Coding
• Abstract summary: ReLook is an agentic, vision-grounded reinforcement learning framework.<n>It empowers an agent to close a robust generate--diagnose--refine loop.<n>It consistently outperforms strong baselines in vision-grounded front-end code generation.
• Score: 35.30369059154906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Large Language Models (LLMs) excel at algorithmic code generation, they struggle with front-end development, where correctness is judged on rendered pixels and interaction. We present ReLook, an agentic, vision-grounded reinforcement learning framework that empowers an agent to close a robust generate--diagnose--refine loop by invoking a multimodal LLM (MLLM) as a tool. During training, the agent uses the MLLM-in-the-loop both as a visual critic--scoring code with screenshots--and as a source of actionable, vision-grounded feedback; a strict zero-reward rule for invalid renders anchors renderability and prevents reward hacking. To prevent behavioral collapse, we introduce Forced Optimization, a strict acceptance rule that admits only improving revisions, yielding monotonically better trajectories. At inference, we decouple the critic and run a lightweight, critic-free self-edit cycle, keeping latency comparable to base decoding while retaining most of the gains. Across three widely used benchmarks, ReLook consistently outperforms strong baselines in vision-grounded front-end code generation, highlighting the benefits of agentic perception, visual rewards, and training-inference decoupling.

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