arXiv Machine Learning July 30, 2026 By Nicolas Gutowski, Fabien Chhel, Alexandre Letard, Sylvain Lamprier

Top-$k$ Pareto Bandits: Hypervolume Regret for Multi-Objective Slate Selection

Read the original on arXiv Machine Learning →

arXiv:2607. 26273v1 Announce Type: new Abstract: We consider a stochastic multi-objective bandit problem where, at each round, the agent selects a slate of $k$ arms and observes their $d$-dimensional reward vectors under semi-bandit feedback.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 6

Cost-Aware Multi-Objective Bandits: Theory and Application to Budgeted LLM Configuration Evaluation

arXiv:2608. 04333v1 Announce Type: new Abstract: Large language model (LLM) configuration evaluation is challenging due to limited evaluation budgets, varying costs, and multiple competing objectives.

By Bo Xue, Zhi Hong, Jiayi Li, Yuanyu Wan, Ji Cheng, Shuang Qiu

llms reinforcement-learning

arXiv Machine Learning

Jun 30

Optimal Regret for Single Index Bandits

arXiv:2605. 09454v2 Announce Type: replace-cross Abstract: We study the $\textit{single-index bandit}$ problem, where rewards depend on an unknown one-dimensional projection of high-dimensional contexts through an unknown reward function.

By Devdan Dey, Sujoy Bhore, Avishek Ghosh

reinforcement-learning

arXiv Machine Learning

Jul 23

Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence

arXiv:2607. 19854v1 Announce Type: new Abstract: We study horizon-free regret minimization for finite-horizon time-homogeneous tabular Markov decision processes with $S$ states, $A$ actions, horizon $H$, and per-trajectory total reward bounded by $1$.

By Runlong Zhou, Zihan Zhang, Maryam Fazel, Simon S. Du

reinforcement-learning

arXiv Machine Learning

Jul 16

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

arXiv:2607. 13686v1 Announce Type: new Abstract: We study the contextual combinatorial semi-bandit (CCSB) problem with general reward function approximation.

By Hao Qin, Chicheng Zhang

reinforcement-learning benchmarks

Hugging Face Trending Papers

Jul 15

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

We study the contextual combinatorial semi-bandit (CCSB) problem with general reward function approximation. At each round, the learner observes a context, selects a combinatorial action consisting of a subset of basic arms, and receives the reward of each selected arm; the goal is to maximize the cumulative reward over time.

reinforcement-learning benchmarks

arXiv Machine Learning

1d ago

Does 1/2-Tsallis-INF Also Work Well for Best-Arm Identification?

arXiv:2608. 15365v1 Announce Type: new Abstract: Regret minimization (RM) and best-arm identification (BAI) are two fundamental objectives in multi-armed bandits.

By Jingxin Zhan, Yuze Han, Zhihua Zhang

diffusion safety

Related stories

Cost-Aware Multi-Objective Bandits: Theory and Application to Budgeted LLM Configuration Evaluation

Optimal Regret for Single Index Bandits

Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

Does 1/2-Tsallis-INF Also Work Well for Best-Arm Identification?