Feedback-Driven Prompt Refinement for Image Editing via Self-Distillation

Instruction-guided image editing enables users to modify images through natural-language instructions, yet editing quality remains sensitive to the prompts used to control the editor, creating a gap between user intent and model behavior. Existing iterative refinement methods often explicitly evaluate intermediate editing results before generating revised instructions, coupling evaluation and refinement within the deployed policy. Such intermediate assessments can misguide subsequent refinement when inaccurate, while also introducing additional supervision and inference requirements. We propose $\text{FeedEdit}$, a feedback-driven prompt-refinement framework that learns refinement decisions directly from editing outcomes. FeedEdit formulates prompt refinement as an adaptive stop-or-refine decision problem, where evaluator feedback is used during training to teach the policy whether to stop or how to refine, eliminating the need to explicitly generate an intermediate evaluation at inference. Our key insight is that outcome feedback can supervise the policy in two complementary ways: *causal feedback* evaluates the current edit and guides the construction of stopping and refinement targets for offline learning, while *hindsight feedback* evaluates the outcome of a policy-sampled action and provides token-level supervision for online self-distillation. On ImgEdit-Bench and GEdit-Bench-EN, FeedEdit achieves overall scores of 4.54 and 8.13 with FLUX.2-klein-9B, outperforming strong state-of-the-art refinement methods. Further analyses show that FeedEdit learns effective adaptive stop-or-refine behavior and transfers across editors without retraining. We plan to release the dataset, model checkpoints, and code.