From Pixels to Policies: Reinforcing Spatial Reasoning in Language Models for Content-Aware Layout Design

Sha Li, Stefano Petrangeli, Yu Shen, Xiang Chen

Keywords: Spatial Reasoning, Content-Aware Layout Design, Reinforcement Learning, Large Language Models

TL;DR: A reinforcement learning–based framework that empowers LLMs with explicit spatial reasoning for content-aware graphic layout design.

Abstract: 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 transparency 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 state-of-the-art layout generators while requiring fewer annotated samples.

Submission Type: Discovery

Submission Number: 371