VGEdit: Unlocking Video Generation Priors for Reasoning-Informed Image Editing

Image editing has become an important capability of modernvisual generative models. Most existing approaches generate the targetimage directly given an input image and an editing instruction. Whileeffective for simple appearance modifications, this paradigm often failswhen edits require multi-step inference over spatial, temporal, causal, orcommonsense relationships, which entails reasoning about how the sceneshould evolve through a series of plausible intermediate states from thesource image to the desired result. Notably, modern video generationmodels naturally capture such transformation processes: by modelinghow scenes evolve over time, they encode strong temporal coherence andimplicit physical priors. However, directly applying video models to imageediting remains challenging due to the mismatch between editing instruc-tions and video generation objectives. To bridge this gap, we proposeVGEdit, a reinforcement learning framework that empowers video gener-ation models for image editing. Our method consists of two components:(1) Instruction transfer : an MLLM converts the editing instruction into avideo description, reframing the edit as a plausible transformation process.(2) Reinforcement learning: we adapt the video model with reward-basedoptimization, treating intermediate frames as implicit reasoning chainsand the last frame as the target edited image. This formulation eliminatesthe need for ground-truth editing videos and enables direct optimizationfrom rewards that capture instruction faithfulness, contextual consistency,and user preference alignment. Experiments show that VGEdit signifi-cantly outperforms prior methods on reasoning-informed image editingbenchmarks, demonstrating that connecting image editing and videogeneration in a process-centric manner provides a scalable path towardmore physically grounded and reasoning-capable visual editing.