DeCoRL: Decomposed Consistency Reinforcement Learning for Multi-Image Composition
arXiv:2610.39265v1 Announce Type: cross Abstract: Multi-image composition (MICo) is a critical yet challenging capability for large-scale image editing models, where consistency degrades rapidly as the number of reference images increases. While supervised fine-tuning (SFT) can equip models with basic MICo ability, it provides only implicit supervision on the composed image and often fails to learn reliable object-level correspondence across multiple references. We propose DeCoRL, a reinforceme…