In this paper, we first discover that the high within-class similarity in condensed datasets necessitates the use of large-scale soft labels.

For this problem, with either a known or unknown system, we give an efficient sublinear regret algorithm.

Theresultingsearch is costly, and designing suitable scores or tests that capture prior knowledge is difficult.

As large language models gain widespread adoption, running them efficiently becomesacrucialtask.