PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Design

Graphic design forms the cornerstone of modern visual com-munication, serving as a vital medium for promoting cultural and com-mercial events. Recent advances have explored automating this processusing Large Multimodal Models (LMMs), yet existing methods often pro-duce geometrically inaccurate layouts and lack the iterative, layer-specificediting required in professional workflows. To address these limitations,we present PosterCopilot, a framework that advances layout reasoningand controllable editing for professional graphic design. Specifically, weintroduce a progressive three-stage training strategy that equips LMMswith geometric understanding and aesthetic reasoning for layout design,consisting of Perturbed Supervised Fine-Tuning, Reinforcement Learn-ing for Visual-Reality Alignment, and Reinforcement Learning from Aes-thetic Feedback. Furthermore, we develop a complete workflow thatcouples the trained LMM-based design model with generative models,enabling layer-controllable, iterative editing for precise element refine-ment while maintaining global visual consistency. Extensive experimentsdemonstrate that PosterCopilot achieves geometrically accurate and aes-thetically superior layouts, offering fine-grained, layer-wise controllabilityfor professional iterative design.