Embed-RL: Reinforcement Learning for Reasoning-Driven Multimodal Embeddings
Leveraging Multimodal Large Language Models (MLLMs) has become pivotal for advancing Universal Multimodal Embeddings (UME) in addressing diverse cross-modal tasks. Recent studies demonstrate that incorporating generative Chain-of-Thought (CoT) reasoning can substantially enhance task-specific representations compared to discriminative methods. However, the generated reasoning CoTs of existing generative embedding methods are limited to the textual analysis of queries and are irrelevant to the retrieval of the targets. To address these limitations, we propose a reasoning-driven UME framework that integrates Embedder-Guided Reinforcement Learning (EG-RL) to optimize the Reasoner to produce evidential Traceability CoT (T-CoT). Our key contributions are threefold: (1) We design an EG-RL framework where the Embedder provides explicit supervision to the Reasoner, ensuring the generated CoT traces are aligned with embedding tasks. (2) We introduce T-CoT, which extracts critical multimodal cues to focus on retrieval-relevant elements and provides multimodal inputs for the Embedder. (3) With limited computational resources, our framework outperforms the pioneering embedding model on both MMEB-V2 and UVRB benchmarks. The integration of multimodal evidence in structured reasoning, paired with retrieval-oriented alignment, effectively strengthens cross-modal semantic consistency and boosts the fine-grained matching capability of the model as well as the generalization across complex scenarios. Our work demonstrates that targeted reasoning optimization can significantly improve multimodal embedding quality, providing a practical and efficient solution for reasoning-driven UME development.
6993db6a50fb2c0be4783ce8
Haonan Jiang
6993db6a50fb2c0be4783ce9
Yuji Wang
claimed_verified
2026-02-17T15:53:53.575Z
659bb678e57c59004625c624
/avatars/32b395c3504acb1fe29cceb65508b351.svg
Voyage_Wang
VoyageWang
user
VoyageWang
6993db6a50fb2c0be4783cea
Yongjie Zhu
6993db6a50fb2c0be4783ceb
Xin Lu
6993db6a50fb2c0be4783cec
Wenyu Qin
6993db6a50fb2c0be4783ced
Meng Wang
6993db6a50fb2c0be4783cee
Pengfei Wan
6993db6a50fb2c0be4783cef
Yansong Tang
Embed-RL: Reinforcement Learning for Reasoning-Driven Multimodal Embeddings
Leveraging Multimodal Large Language Models (MLLMs) has become pivotal for advancing Universal Multimodal Embeddings (UME) in addressing diverse cross-modal tasks. Recent studies demonstrate that incorporating generative Chain-of-Thought (CoT) reasoning can substantially enhance task-specific representations compared to discriminative methods. However, the generated reasoning CoTs of existing generative embedding methods are limited to the textual analysis of queries and are irrelevant to the retrieval of the targets. To address these limitations, we propose a reasoning-driven UME framework that integrates Embedder-Guided Reinforcement Learning (EG-RL) to optimize the Reasoner to produce evidential Traceability CoT (T-CoT). Our key contributions are threefold: (1) We design an EG-RL framework where the Embedder provides explicit supervision to the Reasoner, ensuring the generated CoT traces are aligned with embedding tasks. (2) We introduce T-CoT, which extracts critical multimodal cues to focus on retrieval-relevant elements and provides multimodal inputs for the Embedder. (3) With limited computational resources, our framework outperforms the pioneering embedding model on both MMEB-V2 and UVRB benchmarks. The integration of multimodal evidence in structured reasoning, paired with retrieval-oriented alignment, effectively strengthens cross-modal semantic consistency and boosts the fine-grained matching capability of the model as well as the generalization across complex scenarios. Our work demonstrates that targeted reasoning optimization can significantly improve multimodal embedding quality, providing a practical and efficient solution for reasoning-driven UME development.
A reasoning-driven universal multimodal embedding framework integrates embedder-guided reinforcement learning with traceability chain-of-thought to enhance cross-modal semantic consistency and retrieval performance.
Multimodal Large Language Models Universal Multimodal Embeddings Chain-of-Thought reasoning Embedder-Guided Reinforcement Learning Reasoner Traceability CoT cross-modal tasks multimodal cues retrieval-oriented alignment fine-grained matching