# DeCoRL: Decomposed Consistency Reinforcement Learning for Multi-Image Composition

**Authors:** Zhiqiang Wu, Shuang Sun, Jiale Zhang, Jing Li, Yibo Chen, Xu Tang, Yao Hu  
**Lab:** Xiaohongshu (Little Red Book) · **Published:** 29 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.39265) · [PDF](https://arxiv.org/pdf/2610.39265) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.39265.html)

## 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 reinforcement-learning (RL) framework that improves MICo consistency by decomposing holistic MICo consistency into object-level consistency. DeCoRL first establishes instruction-relevant object correspondences across reference images and localizes the corresponding regions in the generated image. It then uses a VLM-based reward model trained on single-object consistency annotations to produce fine-grained, object-wise feedback, which is aggregated into a multi-object consistency reward to guide RL post-training. We further introduce DeCo-Bench, a benchmark covering compositions from 2 to 6 reference images. Experiments on DeCo-Bench and the public MICo-Bench demonstrate consistent improvements in MICo consistency.

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