ImageEdit-R1: Boosting Multi-Agent Image Editing via Reinforcement Learning

TL;DR

ImageEdit-R1 introduces a multi-agent reinforcement learning framework for complex, context-aware image editing, significantly improving performance over existing models by coordinating specialized agents for nuanced edits.

Contribution

This work presents a novel multi-agent reinforcement learning approach that enables dynamic, goal-oriented image editing through coordinated decision-making among specialized agents.

Findings

Outperforms existing closed-source diffusion models

Achieves better results on multiple image editing datasets

Demonstrates effective multi-agent collaboration in editing tasks

Abstract

With the rapid advancement of commercial multi-modal models, image editing has garnered significant attention due to its widespread applicability in daily life. Despite impressive progress, existing image editing systems, particularly closed-source or proprietary models, often struggle with complex, indirect, or multi-step user instructions. These limitations hinder their ability to perform nuanced, context-aware edits that align with human intent. In this work, we propose ImageEdit-R1, a multi-agent framework for intelligent image editing that leverages reinforcement learning to coordinate high-level decision-making across a set of specialized, pretrained vision-language and generative agents. Each agent is responsible for distinct capabilities--such as understanding user intent, identifying regions of interest, selecting appropriate editing actions, and synthesizing visual content--while reinforcement learning governs their collaboration to ensure coherent and goal-directed behavior. Unlike existing approaches that rely on monolithic models or hand-crafted pipelines, our method treats image editing as a sequential decision-making problem, enabling dynamic and context-aware editing strategies. Experimental results demonstrate that ImageEdit-R1 consistently outperforms both individual closed-source diffusion models and alternative multi-agent framework baselines across multiple image editing datasets.