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University of Cambridge
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Reinforcement learning enables video-LLMs to re-watch and refine answers without the costly overhead of chain-of-thought training, achieving better performance with less computation.
UBD reveals that traditional accuracy metrics can mask critical sample-level discrepancies in model behavior, leading to a more nuanced understanding of data contamination effects.
