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This work proposes Interleaved Distillation and Reinforcement Learning (IDRL), a novel post-training paradigm that tightly integrates on-policy distillation and reinforcement learning within a single training loop and enables more effective knowledge transfer, greater optimization stability, and more precise credit ass...
This work proposes a comprehensive data-curation procedure that efficiently acquires rich medical knowledge data not only from medical imaging but also from extensive medical texts and general domain data and develops a medical-specialized MLLM, Lingshu-Med, which consistently outperforms existing multimodal baselines...
The causes of modal divergence are probed, offering insights into fostering culturally robust MLLMs, and a Multilingual, Multimodal Alignment framework for Cultural grounding evaluation is proposed.
Experiments across video understanding and reasoning benchmarks show that the Evidence-Grounded Self-Teacher framework consistently improves upon Standard OPSD across multiple backbones and achieves performance comparable to GRPO while requiring substantially less training time, establishing an effective and efficient...
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