FLOORA: A Human-Aligned Domain-Specific Language Model for Architectural Design

Abstract:Foundation models are powerful generators, but many engineering domains require structured representations that general-purpose systems handle poorly. We introduce FLOORA (Floor Layout Optimization with RL Alignment), a family of small domain-specific language (DSL) models for architectural layout generation. With specialized data and alignment, our 0.6B model outperforms much larger frontier models, achieving VLM judge win rates up to 92.0% on out-of-distribution real-world buildings and 96.0% on synthetic buildings. Human evaluations further corroborate these results, with FLOORA selected as the best model in 89.3% of evaluations. FLOORA combines a token-efficient DSL, custom tokenization, domain-specific pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL) with learned human-preference and verifiable rewards. This pipeline improves architectural and geometric validity, supported by extensive empirical evaluation and ablation studies. Although focused on architecture, our results suggest that similar domain-specific recipes may be useful in other engineering domains with structured, verifiable outputs. Datasets, models, and inference code are available at this https URL.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.36064 [cs.LG]

(or arXiv:2609.36064v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.36064 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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