This paper studies the theoretical framework of the alignment process of generative models
with Reinforcement Learning from Human Feedback (RLHF). We consider a standard …

In reinforcement learning (RL), balancing exploration and exploitation is crucial for
achieving an optimal policy in a sample-efficient way. To this end, existing sample-efficient …

A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the\emph {curse of
multiagency}, where the description length of the game as well as the complexity of many …

This paper studies the theoretical framework of the alignment process of generative models
with Reinforcement Learning from Human Feedback (RLHF). We consider a standard …

Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-
collected dataset without further interactions with the environment. While various algorithms …

Reinforcement Learning from Human Feedback (RLHF) learns from the preference signal
provided by a probabilistic preference model, which takes a prompt and two responses as …

We study sample efficient reinforcement learning (RL) under the general framework of
interactive decision making, which includes Markov decision process (MDP), partially …

We seek to understand what facilitates sample-efficient learning from historical datasets for
sequential decision-making, a problem that is popularly known as offline reinforcement …

This paper investigates posterior sampling algorithms for competitive reinforcement learning
(RL) in the context of general function approximations. Focusing on zero-sum Markov games …

In online reinforcement learning (online RL), balancing exploration and exploitation is
crucial for finding an optimal policy in a sample-efficient way. To achieve this, existing …