arXiv:2611.03388 · v1
LAND: Learning an Adaptive Number of Draws for Bayesian Best-of-N Selection
摘要
Best-of-N decoding improves generation quality by sampling multiple candidates and selecting the best, but it allocates equal compute to sources regardless of how much they benefit from additional sampling: the expected benefit is small for sources with low-variance score distributions and potentially much larger for those with high-variance distributions. To address this shortcoming, we propose LAND (Learning an Adaptive Number of Draws), which dynamically allocates samples according to their posterior-predictive marginal value. LAND learns a discrete empirical mixture of source-conditioned score distributions from calibration data, updates its belief about a source as completion scores are observed, and stops sampling when the expected improvement of another draw falls below a shared compute price. Across machine-translation experiments with multiple models and language pairs, LAND achieves better MetricX scores than fixed best-of-N at matched average compute. Conversely, at matched translation quality, LAND requires approximately 20-40% fewer candidate generations. Adaptive allocation therefore provides a simple way to retain the benefits of best-of-N while substantially improving its compute efficiency.
作者备注：10 pages. Published in Proceedings of the Eleventh Conference on Machine Translation (WMT 2026)
期刊信息：Proc. of the Eleventh Conference on Machine Translation (WMT 2026), pp. 520-529, Association for Computational Linguistics, 2026