From Plans to Pixels: Learning to Plan and Orchestrate for Open-Ended Image Editing

Modern image editing models produce realistic results but struggle with abstract, multi-step instructions (e.g., “make this advertisement more vegetarian-friendly”). Prior agent-based methods decompose such tasks but rely on handcrafted pipelines or teacher-imitation, limiting flexibility and decoupling learning from actual editing outcomes. We propose an experiential framework for long-horizon image editing, where a planner generates structured atomic decompositions and an orchestrator selects tools and regions to execute each step. A vision–language judge provides outcome-based rewards for instruction adherence and visual quality. The orchestrator is trained to maximize these rewards, and successful trajectories are used to refine the planner. By tightly coupling planning with reward-driven execution, our approach yields more coherent and reliable edits than single-step or rule-based multi-step baselines.