Generative Floor Plan Design with LLMs via Reinforcement Learning with Verifiable Rewards

An AI system for professional floor plan design needs to be able to precisely control room dimensions and areas (quantitative constraints), while also balancing functional considerations and design aesthetics. Existing generative approaches focus primarily on respecting the requested connectivity between rooms, but do not support generating floor plans with 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 enforce both numerical (areas, dimensions) and spatial (topological) constraints. Furthermore, we design a set of constraint adherence metrics to measure how generated floor plans align with user-defined constraints systematically. Our model generates floor plans that satisfy numerical constraints and outperforms existing methods on realism, compatibility, and diversity scores. Specifically, our approach leads to an up to 94\% reduction in compatibility score. Our results demonstrate that LLMs can effectively handle quantitative constraints in structured design tasks, suggesting broader applications for text-based generative modeling.