Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

Reinforcement Learning algorithms that learn from human feedback (RLHF) need to be
efficient in terms of statistical complexity, computational complexity, and query complexity. In …

This paper investigates the problem of generalized linear bandits with heavy-tailed rewards,
whose $(1+\epsilon) $-th moment is bounded for some $\epsilon\in (0, 1] $. Although there …

A crucial task in decision-making problems is reward engineering. It is common in practice
that no obvious choice of reward function exists. Thus, a popular approach is to introduce …

Dueling bandits is a prominent framework for decision-making involving preferential
feedback, a valuable feature that fits various applications involving human interaction, such …

We consider the problem of contextual bandits and imitation learning, where the learner
lacks direct knowledge of the executed action's reward. Instead, the learner can actively …

Aligning large language models (LLM) with human preference plays a key role in building
modern generative models and can be achieved by reinforcement learning from human …

We consider the problem of reward maximization in the dueling bandit setup along with
constraints on resource consumption. As in the classic dueling bandits, at each round the …

Dueling bandits are widely used to model preferential feedback prevalent in many
applications such as recommendation systems and ranking. In this paper, we study the …

Reinforcement Learning from Human Feedback (RLHF) has become a popular approach to
align language models (LMs) with human preferences. This method involves collecting a …