Efficient Best-of-N policy evaluation for inference-time alignment
Read the original on arXiv Machine Learning →The paper introduces a sample‑only framework for evaluating and selecting Best‑of‑N (BoN) inference‑time alignment policies without needing response likelihoods. By exploiting the order‑statistic structure of BoN, the authors express required density ratios through score‑rank probabilities that can be estimated from samples alone, and develop a doubly robust estimator (BoN‑DR) that reuses a shared auxiliary sample pool across candidate budgets. The method provides valid asymptotic inference even with reward estimator misspecification, proves efficiency, and offers two selection rules—maximizing estimated policy value or a lower confidence bound—to avoid over‑optimization and guarantee no harm, as demonstrated on synthetic data and GSM8K.
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