Towards Unified Multimodal Interleaved Generation via Group Relative Policy Optimization

• URL: http://arxiv.org/abs/2603.09538v1
• Date: Tue, 10 Mar 2026 11:49:20 GMT
• Title: Towards Unified Multimodal Interleaved Generation via Group Relative Policy Optimization
• Abstract summary: We propose a reinforcement learning-based post-training strategy to unlock this capability in existing unified models.<n>Our approach jointly models text and image generation within a single decoding trajectory and optimize it with our novel hybrid rewards.<n>Experiments on MMIE and InterleavedBench demonstrate that our approach significantly enhances the quality and coherence of multimodal interleaved generation.
• Score: 35.14373974143734
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unified vision-language models have made significant progress in multimodal understanding and generation, yet they largely fall short in producing multimodal interleaved outputs, which is a crucial capability for tasks like visual storytelling and step-by-step visual reasoning. In this work, we propose a reinforcement learning-based post-training strategy to unlock this capability in existing unified models, without relying on large-scale multimodal interleaved datasets. We begin with a warm-up stage using a hybrid dataset comprising curated interleaved sequences and limited data for multimodal understanding and text-to-image generation, which exposes the model to interleaved generation patterns while preserving its pretrained capabilities. To further refine interleaved generation, we propose a unified policy optimization framework that extends Group Relative Policy Optimization (GRPO) to the multimodal setting. Our approach jointly models text and image generation within a single decoding trajectory and optimizes it with our novel hybrid rewards covering textual relevance, visual-text alignment, and structural fidelity. Additionally, we incorporate process-level rewards to provide step-wise guidance, enhancing training efficiency in complex multimodal tasks. Experiments on MMIE and InterleavedBench demonstrate that our approach significantly enhances the quality and coherence of multimodal interleaved generation.

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