3 papers
cs.LG2026
A Unifying View of Coverage in Linear Off-Policy Evaluation
Philip Amortila, Audrey Huang, Akshay Krishnamurthy +1
Off-policy evaluation (OPE) is a fundamental task in reinforcement learning (RL). In the classic setting of linear OPE, finite-sample guarantees often take the form $$ \textrm{Eval…
cs.AI2025
Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment
Audrey Huang, Adam Block, Qinghua Liu +3
Inference-time computation offers a powerful axis for scaling the performance of language models. However, naively increasing computation in techniques like Best-of-N sampling can…
cs.LG2025
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol
Pai Liu, Lingfeng Zhao, Shivangi Agarwal +4
Holdout validation and hyperparameter tuning from data is a long-standing problem in offline reinforcement learning (RL). A standard framework is to use off-policy evaluation (OPE)…