Whether or Where? Tail Calibration Signals Finite-Budget Reward Hacking in Best-of-N Selection
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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arXiv:2610.19362v1 Announce Type: cross Abstract: Best-of-N sampling is a simple inference-time scaling strategy for large language models, yet its behavior under incomplete reward models remains poo...
The paper introduces TailCert, a method for certifying an upper tail quantile of a policy’s performance using logged contextual‑bandit data. It builds a PAC‑Bayes Bernstein lower‑confidence envelope over a threshold grid and all data‑dependent posteriors, returning an atom‑safe upper confidence bound on the selected posterior’s pooled p‑quantile. The authors provide finite‑sample guarantees, analyze sample complexity on a rare‑action class, and demonstrate the method’s effectiveness through simulations.
The paper evaluates how well typed decision models, which output probability distributions, actually provide reliable guarantees. By comparing open-weight Laya and Jev 1.13 across 190 evaluations, the study finds that point‑estimate thresholds frequently break promised accuracy, while a Beta lower‑bound rule and Learn‑then‑Test approach maintain guarantees more consistently. The work also explores calibration across languages, the impact of noise, data requirements for fine‑tuning, and the limitations of confidence in predicting outcomes or providing faithful explanations.
arXiv:2606. 15600v1 Announce Type: cross Abstract: Cardinality-estimation (CE) research ranks estimators by q-error, yet it is well known that q-error is an imperfect proxy for query-plan quality.
arXiv:2608. 19748v1 Announce Type: cross Abstract: Inference-time selection methods, such as Best-of-N, improve generation by sampling a pool of candidates and selecting the top-ranked completion according to a reward model.
arXiv:2608. 08002v1 Announce Type: new Abstract: Language-model judges and reward models enable scalable supervision, but finite optimization can exploit evaluator errors rather than improve response quality.