UME-R1: Exploring Reasoning-Driven Generative Multimodal Embeddings

• URL: http://arxiv.org/abs/2511.00405v1
• Date: Sat, 01 Nov 2025 05:04:23 GMT
• Title: UME-R1: Exploring Reasoning-Driven Generative Multimodal Embeddings
• Authors: Zhibin Lan, Liqiang Niu, Fandong Meng, Jie Zhou, Jinsong Su,
• Abstract summary: We pioneer the exploration of generative embeddings, unifying embedding tasks within a generative paradigm.<n>We propose UME-R1, a universal multimodal embedding framework consisting of a two-stage training strategy.<n> evaluated on the MMEB-V2 benchmark across 78 tasks spanning video, image, and visual documents.
• Score: 70.60608084375691
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The remarkable success of multimodal large language models (MLLMs) has driven advances in multimodal embeddings, yet existing models remain inherently discriminative, limiting their ability to benefit from reasoning-driven generation paradigm. In this work, we pioneer the exploration of generative embeddings, unifying embedding tasks within a generative paradigm. We propose UME-R1, a universal multimodal embedding framework consisting of a two-stage training strategy: a cold-start supervised fine-tuning equips the model with reasoning capabilities and enables it to generate both discriminative and generative embeddings; a subsequent reinforcement learning enhances reasoning and further optimizes generative embedding quality. This pioneering work reveals four key insights: 1) generative embeddings unlock substantial performance gains over conventional discriminative embeddings by leveraging the powerful generative reasoning capabilities of MLLMs; 2) discriminative and generative embeddings are complementary, whose combined oracle performance far exceeding that of either alone; 3) RL can effectively enhance generative embeddings, establishing a scalable optimization paradigm.; 4) repeated sampling at inference boosts downstream task coverage (pass@k), highlighting the inference-time scalability potential of generative embeddings. Evaluated on the MMEB-V2 benchmark across 78 tasks spanning video, image, and visual documents, UME-R1 significantly outperforms conventional discriminative embedding models and offers a foundation for more interpretable, reasoning-driven generative multimodal embeddings. Our code, models, and datasets will be publicly available at https://github.com/XMUDeepLIT/UME-R1.

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