用大模型加强化学习生成符合尺寸和连接要求的户型图
Generative Floor Plan Design with LLMs via Reinforcement Learning with Verifiable Rewards
- 用文本指令微调大模型，再通过可验证奖励强化学习优化约束满足度
- 生成的户型图在连通性与尺寸约束上达标率超94%提升，真实感更强
- 适合需要精确控制空间布局的建筑设计、室内设计领域
专业级户型设计需精确控制房间尺寸与面积，同时满足房间间连接关系，并保持功能与美学质量。现有生成方法多关注连接关系，但无法处理数值约束。本文提出一种基于文本的户型生成方法：先在真实户型图上微调大语言模型（LLM），再采用可验证奖励的强化学习（RLVR）提升对拓扑与数值约束的遵守程度，避免无效或重叠输出。我们设计了一套约束符合度指标，系统评估生成结果与用户定义约束的一致性。实验表明，该模型能生成满足用户指定连通性与数值约束的户型图，在真实感、兼容性和多样性上优于现有方法。在所有任务中，兼容性指标相对现有方法至少降低94%。结果证明大模型可在该场景有效处理复杂约束，为文本驱动生成建模提供新思路。
原文摘要 · Abstract (English)
An AI system for professional floor plan design must precisely control room dimensions and areas while respecting the desired connectivity between rooms and maintaining functional and aesthetic quality. Existing generative approaches focus primarily on respecting the requested connectivity between rooms, but do not support generating floor plans that respect numerical constraints. We introduce a text-based floor plan generation approach that fine-tunes a large language model (LLM) on real plans and then applies reinforcement learning with verifiable rewards (RLVR) to improve adherence to topological and numerical constraints while discouraging invalid or overlapping outputs. Furthermore, we design a set of constraint adherence metrics to systematically measure how generated floor plans align with user-defined constraints. Our model generates floor plans that satisfy user-defined connectivity and numerical constraints and outperforms existing methods on Realism, Compatibility, and Diversity metrics. Across all tasks, our approach achieves at least a 94% relative reduction in Compatibility compared with existing methods. Our results demonstrate that LLMs can effectively handle constraints in this setting, suggesting broader applications for text-based generative modeling.
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