Interleaving Reasoning for Better Text-to-Image Generation
Authors: name: Wenxuan Huang, name: Shuang Chen, name: Zheyong Xie, name: Shaosheng Cao, name: Shixiang Tang, name: Yufan Shen, name: Qingyu Yin, name: Wenbo Hu, name: Xiaoman Wang, name: Yuntian Tang, name: Junbo Qiao, name: Yue Guo, name: Yao Hu, name: Zhenfei Yin, name: Philip Torr, name: Yu Cheng, name: Wanli Ouyang, name: Shaohui Lin

Unified multimodal understanding and generation models recently have achieve
significant improvement in image generation capability, yet a large gap remains
in instruction following and detail preservation compared to systems that
tightly couple comprehension with generation such as GPT-4o. Motivated by
recent advances in interleaving reasoning, we explore whether such reasoning
can further improve Text-to-Image (T2I) generation. We introduce Interleaving
Reasoning Generation (IRG), a framework that alternates between text-based
thinking and image synthesis: the model first produces a text-based thinking to
guide an initial image, then reflects on the result to refine fine-grained
details, visual quality, and aesthetics while preserving semantics. To train
IRG effectively, we propose Interleaving Reasoning Generation Learning (IRGL),
which targets two sub-goals: (1) strengthening the initial think-and-generate
stage to establish core content and base quality, and (2) enabling high-quality
textual reflection and faithful implementation of those refinements in a
subsequent image. We curate IRGL-300K, a dataset organized into six decomposed
learning modes that jointly cover learning text-based thinking, and full
thinking-image trajectories. Starting from a unified foundation model that
natively emits interleaved text-image outputs, our two-stage training first
builds robust thinking and reflection, then efficiently tunes the IRG pipeline
in the full thinking-image trajectory data. Extensive experiments show SoTA
performance, yielding absolute gains of 5-10 points on GenEval, WISE, TIIF,
GenAI-Bench, and OneIG-EN, alongside substantial improvements in visual quality
and fine-grained fidelity. The code, model weights and datasets will be
released in: https://github.com/Osilly/Interleaving-Reasoning-Generation .