Learning to Generate via Understanding: Understanding-Driven Intrinsic Rewarding for Unified Multimodal Models
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arXiv
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| Format: | Preprint |
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2026
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| author | Pan, Jiadong Li, Liang Peng, Yuxin Tang, Yu-Ming Wang, Shuohuan Sun, Yu Wu, Hua Huang, Qingming Wang, Haifeng |
| author_facet | Pan, Jiadong Li, Liang Peng, Yuxin Tang, Yu-Ming Wang, Shuohuan Sun, Yu Wu, Hua Huang, Qingming Wang, Haifeng |
| contents | Recently, unified multimodal models (UMMs) have made remarkable progress in integrating visual understanding and generation, demonstrating strong potential for complex text-to-image (T2I) tasks. Despite their theoretical promise, a persistent capability gap exists: UMMs typically exhibit superior visual understanding but comparatively weaker generative capabilities. This discrepancy arises largely from the intrinsic decoupling between the understanding and generation processes. While a UMM can accurately interpret fine-grained visual details, it often struggles to produce semantically coherent images from complex textual prompts. To address this challenge, we explore UMMs' internal understanding capability to enhance generation quality. We propose a token-level intrinsic text-image alignment reward mechanism, GvU, enabling the UMM to act simultaneously as teacher and student: it evaluates its own outputs using the understanding branch to guide the generations accordingly. Building upon this, we design a self-supervised reinforcement learning framework, allowing UMMs to iteratively improve their generation quality through understanding-based intrinsic reward signals--without reliance on external supervision. Experimental results show that our method substantially boosts UMMs' generation, which in turn strengthens their fine-grained visual understanding, narrowing the capability gap between UMMs' visual understanding and generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06043 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning to Generate via Understanding: Understanding-Driven Intrinsic Rewarding for Unified Multimodal Models Pan, Jiadong Li, Liang Peng, Yuxin Tang, Yu-Ming Wang, Shuohuan Sun, Yu Wu, Hua Huang, Qingming Wang, Haifeng Computer Vision and Pattern Recognition Recently, unified multimodal models (UMMs) have made remarkable progress in integrating visual understanding and generation, demonstrating strong potential for complex text-to-image (T2I) tasks. Despite their theoretical promise, a persistent capability gap exists: UMMs typically exhibit superior visual understanding but comparatively weaker generative capabilities. This discrepancy arises largely from the intrinsic decoupling between the understanding and generation processes. While a UMM can accurately interpret fine-grained visual details, it often struggles to produce semantically coherent images from complex textual prompts. To address this challenge, we explore UMMs' internal understanding capability to enhance generation quality. We propose a token-level intrinsic text-image alignment reward mechanism, GvU, enabling the UMM to act simultaneously as teacher and student: it evaluates its own outputs using the understanding branch to guide the generations accordingly. Building upon this, we design a self-supervised reinforcement learning framework, allowing UMMs to iteratively improve their generation quality through understanding-based intrinsic reward signals--without reliance on external supervision. Experimental results show that our method substantially boosts UMMs' generation, which in turn strengthens their fine-grained visual understanding, narrowing the capability gap between UMMs' visual understanding and generation. |
| title | Learning to Generate via Understanding: Understanding-Driven Intrinsic Rewarding for Unified Multimodal Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.06043 |