Bridging Interleaved Multi-Modal Reasoning as a Unified Decision Process
Unified multi-modal models (UMMs) have shown promising interleaved text-image reasoning capabilities, yet effectively optimizing such multi-turn generation via reinforcement learning (RL) remains an open challenge. Existing approaches apply RL exclusively to text steps, relegating image generation to supervised surrogates, preventing policy gradients from propagating through the full interleaved trajectory across heterogeneous modalities. This leaves the potential of RL for UMMs largely untapped. In the paper, we introduce \textbf{BRAID} (\textbf{B}ridging inte\textbf{R}le\textbf{A}ved mult\textbf{I}-modal reasoning as a unified \textbf{D}ecision process), a simple framework that casts mu...
| Authors | Zican Hu, Xuyang Hu, Yiming Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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