We introduce LaySPA, a reinforcement learning framework that equips large language models (LLMs) with explicit and interpretable spatial reasoning for content-aware graphic layout design. LaySPA addresses two key challenges: LLMs' limited spatial reasoning and the lack of opacity in design decision making. Instead of operating at the pixel level, we reformulate layout design as a policy learning problem over a structured textual spatial environment that explicitly encodes canvas geometry, element attributes, and inter-element relationships. LaySPA produces dual-level outputs comprising interpretable reasoning traces and structured layout specifications, enabling transparent and controllable design decision making. Layout design policy is optimized via a multi-objective spatial critique that decomposes layout quality into geometric validity, relational coherence, and aesthetic consistency, and is trained using relative group optimization to stabilize learning in open-ended design spaces. Experiments demonstrate that LaySPA improves structural validity and visual quality, outperforming larger proprietary LLMs and achieving performance comparable to specialized SOTA layout generators while requiring fewer annotated samples and reduced latency.

翻译：我们提出了LaySPA，一种强化学习框架，旨在为大型语言模型（LLMs）配备显式且可解释的空间推理能力，以用于内容感知的图形布局设计。LaySPA解决了两个关键挑战：LLMs有限的空间推理能力以及设计决策过程缺乏透明度的问题。我们并非在像素级别进行操作，而是将布局设计重新表述为一个在结构化文本空间环境上的策略学习问题，该环境显式编码了画布几何、元素属性以及元素间关系。LaySPA产生包含可解释推理轨迹和结构化布局规范的双层输出，从而实现透明且可控的设计决策。布局设计策略通过一个多目标空间评判器进行优化，该评判器将布局质量分解为几何有效性、关系一致性和美学一致性，并采用相对群体优化进行训练，以在开放式设计空间中稳定学习过程。实验表明，LaySPA提高了结构有效性和视觉质量，其表现优于更大的专有LLMs，并且达到了与专用SOTA布局生成器相当的性能，同时需要更少的标注样本并降低了延迟。