让AI当室内设计师：先学会挑毛病，再学会摆家具
现在的AI生成3D室内场景，要么靠通用模型瞎摆，要么只从静态图里学，摆出来的东西看着像样，但物理上穿墙、功能上没法用。这篇让AI先学“批评”再学“设计”：先给它看大量场景，训练它判断哪里不合理——椅子悬空、沙发挡住门、桌子摆得没法坐人——然后再让它动手设计，边摆边自己挑毛病、改错。结果在物理合理性、功能正确性、布局连贯性上都明显超过老方法。它不是你明天就能用的工具，但方向值得注意：让AI学会自我批评，可能比堆更多数据更管用。
📄 原文摘要(英文)
Generating complete, coherent, and usable 3D indoor scenes from natural language requires strong design intelligence: the ability to design high-quality scenes and critique their quality. However, existing approaches either rely on general-purpose models lacking domain-specific design experience or learn from static scenes. In this work, we introduce Scene-Designer, an agentic framework that learns design intelligence through complementary design and critique experiences. We construct two datasets: Scene-OTA-35K, which provides Observation--Thought--Action supervision for learning scene design and efficient error correction, and Scene-OTC-15K, which provides Observation--Thought--Critic supervision for learning scene quality critique across physical validity, functional correctness, and layout coherence. Based on these experiences, we propose Scene-GRPO, a Critic-guided GRPO framework that improves design decisions and reduces unnecessary trial-and-error corrections. Extensive experiments demonstrate that Scene-Designer achieves superior performance in physical validity, functional correctness, and layout coherence, while the learned Scene-Critic significantly outperforms general-purpose models in scene critique. Moreover, test-time Critic guidance further improves scene generation, showing that the Critic can provide useful guidance beyond training. Additional analysis suggests a trend of mutual enhancement between scene design and critique abilities.