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The paper introduces DDA-Thinker, a framework for reasoning-driven image editing that decouples and independently optimizes a planning module (Thinker) from a fixed generative model (Editor). To guide the Thinker, they propose a dual-atomic reinforcement learning framework with cognitive and visual atomic rewards derived from verifiable checklists synthesized using source image, user instruction, and rational reference descriptions. Experiments on RISE-Bench and KRIS-Bench show that DDA-Thinker significantly improves performance, enabling a community model to compete with proprietary models.
Decoupling the "Thinker" from the "Editor" in image editing allows targeted optimization of reasoning, leading to performance competitive with strong proprietary models using a fixed generative model.
Recent image editing models have achieved strong visual fidelity but often struggle with tasks requiring complex reasoning. To investigate and enhance the reasoning-grounded planning for image editing, we propose DDA-Thinker, a Thinker-centric framework designed for the independent optimization of a planning module (Thinker) over a fixed generative model (Editor). This decoupled Thinker-centric paradigm facilitates a controlled analysis of the planning module and makes its contribution under a fixed Editor easier to assess. To effectively guide this Thinker, we introduce a dual-atomic reinforcement learning framework. This framework decomposes feedback into two distinct atomic rewards implemented through verifiable checklists: a cognitive-atomic reward to directly assess the quality of the Thinker's executable plan, which serves as the actionable outcome of the Thinker's reasoning, and a visual-atomic reward to assess the final image quality. To improve checklist quality, our checklist synthesis is grounded not only in the source image and user instruction but also in a rational reference description of the ideal post-edit scene. To support this training, we further develop a two-stage data curation pipeline that first synthesizes a diverse and reasoning-focused dataset, then applies difficulty-aware refinement to curate an effective training curriculum for reinforcement learning. Extensive experiments on reasoning-driven image editing benchmarks, including RISE-Bench and KRIS-Bench, demonstrate that our approach substantially improves overall performance. Our method enables a community model to achieve results competitive with strong proprietary models, highlighting the practical potential of Thinker-centric optimization under a fixed-editor setting.
