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The Hong Kong University of Science and Technology (
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Cross-tokenizer On-Policy Distillation achieves superior efficiency and flexibility, enabling knowledge transfer between diverse model families without the constraints of shared tokenizers.
MLLM training gets a 1.36x speed boost with Dynamic Hybrid Parallelism (DHP), which adaptively optimizes parallelism strategies to handle the data heterogeneity that plagues multimodal datasets.
