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.

Summary generated by The Flow from the publisher's feed. 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

1d ago

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

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