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Reinforcement learning with verifiable rewards (RLVR) plays a pivotal role in improving the reasoning ability of large language models. …

Vector quantization is a fundamental primitive for scalable machine learning systems, enabling memory-efficient storage, fast …

We study optimal experimental design for multinomial logit (MNL) bandits, where an agent repeatedly selects a subset of $K$ items from …

We study online preference-based reinforcement learning (PbRL) with the goal of improving sample efficiency. While a growing body of …

We revisit the contextual cascading bandit, where a learning agent recommends an ordered list ($\text{\textit{cascade}}$) of items and …

In this paper, we consider combinatorial reinforcement learning with preference feedback,where a learning agent sequentially offers an …

In this paper, we propose an improved online confidence bound for multinomial logistic (MNL) models and apply this result to MNL …

In this paper, we study the contextual multinomial logit (MNL) bandit problem in which a learning agent sequentially selects an …

We study reinforcement learning with multinomial logistic (MNL) function approximation where the underlying transition probability …

In this paper, we first challenge the common premise that linear MDPs always induce performance guarantees independent of the state …