Researchers have identified a critical limitation in blockwise distillation methods used for architecture search, particularly in models like DeiT-S and DeiT-B. These methods, which score individual replacement blocks additively, begin to break down significantly when compression levels reach 55-40% of the original block-latency budgets. The study reveals that this breakdown is largely due to metric and functional components of the KL additivity error, which vary in their impact depending on the model architecture. A proposed hybrid search method, which ranks moves using a logit-sum predictor and measures only the best candidates, offers a more efficient and often superior alternative to existing quadratic programming approaches. AI
IMPACT Identifies limitations in current architecture search techniques, potentially guiding development of more robust methods for model compression.
RANK_REASON Academic paper detailing a new finding about model architecture search methods. [lever_c_demoted from research: ic=1 ai=1.0]
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