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Black-box optimization problems are ubiquitous across science and engineering, often dealing with expensive objective functions. This objective latency has two consequences during optimization: (i) the objective evaluation dominates execution time, and (ii) sample-efficient algorithms are crucial to accelerate developm...
RM-EVAL is introduced, a reward model trained on human preference data from SEEDA, as a reference-free meta-evaluator that predicts human-like quality judgments at both full-sequence and partial-sequence levels and can be used as a learning signal to improve GEC generation via Reward-Guided Text Generation (RGTG), whic...
This work demonstrates the suboptimality of standard MF-BO algorithms in the real-world scenarios above, even under ideal assumptions, and mitigate this problem by incorporating historical high-fidelity data accompanied by task descriptors---which can be explicitly given or extracted from unstructured metadata.
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