EditThinker: Unlocking Iterative Reasoning for Any Image Editor
Hongyu Li, Manyuan Zhang, Dian Zheng, Ziyu Guo, Yimeng Jia, Kaituo Feng, Hao Yu, Yexin Liu, Yan Feng, Peng Pei, Xunliang Cai, Linjiang Huang, Hongsheng Li, Si Liu

TL;DR
EditThinker introduces an iterative, deliberative framework for instruction-based image editing, enabling models to critique and refine edits through multiple cycles, significantly enhancing instruction-following accuracy.
Contribution
The paper presents a novel deliberative editing framework with a single multi-modal large language model that iteratively critiques and refines image editing instructions, improving performance over existing methods.
Findings
Significant improvement in instruction-following accuracy across four benchmarks.
Effective use of reinforcement learning to align reasoning with editing actions.
Demonstrated generalization to any image editing model.
Abstract
Instruction-based image editing has emerged as a prominent research area, which, benefiting from image generation foundation models, have achieved high aesthetic quality, making instruction-following capability the primary challenge. Existing approaches improve instruction adherence via supervised or reinforcement learning, yet single-turn success rates remain limited due to inherent stochasticity and a lack of deliberation. In this work, we propose a deliberative editing framework to 'think' while they edit, which simulates the human cognitive loop by iteratively executing a Think-while-Edit cycle: Critiquing results and Refining instructions , followed by Repeating the generation until satisfactory. Specifically, we train a single MLLM, EditThinker, to act as the reasoning engine of this framework, which jointly produce the critique score, reasoning process, and refined instructions.…
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Taxonomy
TopicsVisual Attention and Saliency Detection · Generative Adversarial Networks and Image Synthesis · Multimodal Machine Learning Applications
