Best-of-Tails: Bridging Optimism and Pessimism in Inference-Time Alignment
Inference-time alignment effectively steers large language models (LLMs) by generating multiple candidates from a reference model and selecting among them with an imperfect reward model. However, current strategies face a fundamental dilemma: ``optimistic'' approaches like Best-of-$N$ suffer from reward hacking, while ``pessimistic'' regularized methods often stifle the exploration needed to discover high-quality responses. In this work, we formalize this trade-off through the lens of regret minimization, demonstrating that the optimal strategy depends critically on the tail behavior of the reward distribution. We show theoretically that light-tailed regimes favor optimism to unearth high...
| Authors | Hsiang Hsu, Eric Lei, Chun-Fu Chen |
|---|---|
| Primary category | cs.AI |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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