ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web
  Coding
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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Yuhang Li
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Chenchen Zhang
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2025-10-14T07:30:13.082Z
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/avatars/b131b7c4ce5216708ca4a678f35ead0a.svg
Chenchen Zhang
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68edc640de1fee572713a8f5
Ruilin Lv
68edc640de1fee572713a8f6
Ao Liu
68edc640de1fee572713a8f7
Ken Deng
68edc640de1fee572713a8f8
Yuanxing Zhang
68edc640de1fee572713a8f9
Jiaheng Liu
68edc640de1fee572713a8fa
Wiggin Zhou
68edc640de1fee572713a8fb
Bo Zhou
ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web
  Coding
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.
ReLook, a vision-grounded reinforcement learning framework, enhances front-end code generation by integrating a multimodal LLM for visual feedback and forced optimization, outperforming existing methods.
Large Language Models LLMs front-end development reinforcement learning agentic vision-grounded multimodal LLM MLLM visual critic vision-grounded feedback Forced Optimization behavioral collapse self-edit cycle vision-grounded front-end code generation