3D-Layout-R1: Structured Reasoning for Language-Instructed Spatial Editing

Large Language Models (LLMs) and Vision Language Mod-els (VLMs) have shown impressive reasoning abilities, yet they strugglewith spatial understanding and layout consistency when performing fine-grained visual editing. We introduce a Structured Reasoning frameworkthat performs text-conditioned spatial layout editing via scene-graph rea-soning. Given an input scene graph and a natural-language instruction,the model reasons over the graph to generate an updated scene graphthat satisfies the text condition while maintaining spatial coherence. Byexplicitly guiding the reasoning process through structured relationalrepresentations, our approach improves both interpretability and controlover spatial relationships. We evaluate our method on a new text-guidedlayout editing benchmark encompassing sorting, spatial alignment, androom-editing tasks. Our training paradigm yields an average 15% im-provement in IoU and 25% reduction in center-distance error comparedto Chain of thought Fine-tuning (CoT-SFT) and vanilla GRPO base-lines. Compared to SOTA zero-shot LLMs, our best models achieve upto 20% higher mIoU, demonstrating markedly improved spatial precision.