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Parameter-efficient adaptation methods can match full fine-tuning gains for tabular foundation models, while selective aggregation offers a clear path to improved predictions over ensemble averaging.
A novel inference-time sampling strategy unlocks frontier LLM reasoning capabilities in smaller models by resolving the exploration-exploitation dilemma.
