AI RESEARCH
Embed-RL: Reinforcement Learning for Reasoning-Driven Multimodal Embeddings
arXiv CS.CV
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ArXi:2602.13823v3 Announce Type: replace Leveraging Multimodal Large Language Models (MLLMs) has become pivotal for advancing Universal Multimodal Embeddings (UME) in addressing diverse cross-modal tasks. Recent studies nstrate 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.