arXiv AI August 10, 2026 By Shion Ishikawa, Pablo Loyola, Young-joo Chung, Yun Ching Liu

Progressive Content Refinement with Decaying Reward Joint LinUCB

arXiv:2608. 06750v1 Announce Type: cross Abstract: Iterative refinement has significantly enhanced Large Language Model (LLM) performance; however, existing methods ranging from feedback-based Self-Refine to traditional bandit approaches often rely on static options or overlook the saturation effect.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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

arXiv AI

Aug 18

Semantic Bandits: In-Context Exploration-Exploitation is Biased by Semantic Priors

arXiv:2608. 16707v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in settings that require sophisticated environmental exploration.

By David Eric Austin, Kaheer Suleman, Jackie Chi Kit Cheung

llms agents reinforcement-learning safety

arXiv Machine Learning

Jun 2

Bandit Simulation for Average Reward Inference

arXiv:2606. 00913v1 Announce Type: cross Abstract: Multi-arm bandit algorithms are increasingly used in online platforms, clinical trials, and social science experiments, but valid statistical inference on their performance remains an open challenge.

By Samya Praharaj, Chih-Yu Chang, Koulik Khamaru, Kelly W. Zhang

reinforcement-learning

arXiv Machine Learning

Jun 2

Finite-Time Regret Analysis of Retry-Aware Bandits

arXiv:2605. 20854v2 Announce Type: replace Abstract: We study a stochastic bandit algorithm motivated by retry-aware objectives that value the best outcome among multiple attempts, such as pass@$k$ and max@$k$.

By Bingkui Tong, Junpei Komiyama, Soichiro Nishimori, Paavo Parmas

reinforcement-learning

arXiv Machine Learning

Jun 2

A Direct Approach for Handling Contextual Bandits with Latent State Dynamics

arXiv:2604. 08149v2 Announce Type: replace Abstract: We consider a linear contextual bandit model where contexts and rewards are governed by a finite hidden Markov chain.

By Zhen Li (LMO, CELESTE, HEC Paris), Gilles Stoltz (LMO, CELESTE, HEC Paris)

reinforcement-learning

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Related stories

Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing

Impatient Bandits: Optimizing for the Long-Term Without Delay

Semantic Bandits: In-Context Exploration-Exploitation is Biased by Semantic Priors

Bandit Simulation for Average Reward Inference

Finite-Time Regret Analysis of Retry-Aware Bandits

A Direct Approach for Handling Contextual Bandits with Latent State Dynamics