We provide a theoretical framework for Reinforcement Learning with Human Feedback
(RLHF). We show that when the underlying true reward is linear, under both Bradley-Terry …

People often ask others for restaurant recommendations as a way to discover new dining
experiences. This makes restaurant recommendation an exciting scenario for recommender …

We consider the problem of preference-based reinforcement learning (PbRL), where, unlike
traditional reinforcement learning (RL), an agent receives feedback only in terms of 1 bit …

We consider the problem of preference based reinforcement learning (PbRL), where, unlike
traditional reinforcement learning, an agent receives feedback only in terms of a 1 bit (0/1) …

In machine learning, the notion of multi-armed bandits refers to a class of online learning
problems, in which an agent is supposed to simultaneously explore and exploit a given set …

We study the $ K $-armed contextual dueling bandit problem, a sequential decision making
setting in which the learner uses contextual information to make two decisions, but only …

We study the problem of $ K $-armed dueling bandit for both stochastic and adversarial
environments, where the goal of the learner is to aggregate information through relative …

Reinforcement Learning from Human Feedback (RLHF) is a pivotal technique that aligns
language models closely with human-centric values. The initial phase of RLHF involves …

The dueling bandits problem is an online learning framework for learning from pairwise
preference feedback, and is particularly well-suited for modeling settings that elicit …

The dueling bandits problem is an online learning framework where learning happens “on-
thefly” through preference feedback, ie, from comparisons between a pair of actions. Unlike …