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Amortized batch black-box optimization via flow matching generates variable-size batches in constant time, outperforming traditional methods on expensive optimization tasks.
Addressing the mismatch in pretraining tasks, this approach significantly boosts the performance of tabular foundation models in molecular optimization tasks.