arXiv AI August 5, 2026 By Alessio Russo, Yin-Ching Lee, Ryan Welch, Aldo Pacchiano

In-Context Pure Exploration in Continuous Decision Spaces

arXiv:2602. 17976v2 Announce Type: replace-cross Abstract: In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to identify an unknown ground-truth hypothesis with as few queries as possible.

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reinforcement-learning

arXiv Machine Learning

Jul 31

On-Policy and Off-Policy Learning for Large Action Spaces

arXiv:2607. 28408v1 Announce Type: new Abstract: This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback.

By Imad Aouali

agents diffusion safety

arXiv Machine Learning

Jul 13

Optimal Top-$k$ Identification from Pairwise Comparisons

arXiv:2607. 08979v1 Announce Type: new Abstract: We study the active learning problem of fixed-confidence top-$k$ identification from noisy pairwise comparisons.

By Motti Goldberger, Nils Rudi

reinforcement-learning

arXiv Machine Learning

Aug 12

Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits

arXiv:2307. 03587v4 Announce Type: replace Abstract: In non-stationary linear contextual bandits, existing efficient algorithms typically rely on the Weighted Regularized Least-Squares (WRLS) estimator.

By Nicklas Werge, Yi-Shan Wu, Abdullah Akg\"ul, Melih Kandemir

benchmarks

arXiv Statistics ML

6d ago

Foundations of Reinforcement Learning and Interactive Decision Making

arXiv:2312.16730v2 Announce Type: replace-cross Abstract: Interactive decision making is the problem of learning to act well in an unknown environment, using the data that one's own actions generate...

By Dylan J. Foster, Alexander Rakhlin

reinforcement-learning robotics

arXiv AI

Jul 13

Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing

arXiv:2607. 09015v1 Announce Type: cross Abstract: We study contextual bandit problems with correlated arms and access to surrogate reward signals produced by a machine learning model, motivated by applications such as large language model (LLM) routing.

By Ajay Narayanan Sridhar, Ronak Singh, Mehrdad Mahdavi, Vijaykrishnan Narayanan

llms reinforcement-learning benchmarks

arXiv AI

Jun 24

Impatient Bandits: Optimizing for the Long-Term Without Delay

arXiv:2501. 07761v2 Announce Type: replace-cross Abstract: Increasingly, recommender systems are tasked with improving users' long-term satisfaction.

By Kelly W. Zhang, Thomas Baldwin-McDonald, Kamil Ciosek, Lucas Maystre, Daniel Russo

reinforcement-learning

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On-Policy and Off-Policy Learning for Large Action Spaces

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Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing

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