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

We investigate the problem of corruption robustness in offline reinforcement learning (RL)
with general function approximation, where an adversary can corrupt each sample in the …

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

Understanding when reinforcement learning algorithms can make successful off-policy
predictions—and when the may fail to do so–remains an open problem. Typically, model …

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 …

Offline reinforcement learning (RL) presents a promising approach for learning reinforced
policies from offline datasets without the need for costly or unsafe interactions with the …

Lipschitz bandit is a variant of stochastic bandits that deals with a continuous arm set
defined on a metric space, where the reward function is subject to a Lipschitz constraint. In …

Linear contextual bandits represent a fundamental class of models with numerous real-
world applications, and it is critical to develop algorithms that can effectively manage noise …

Offline reinforcement learning (RL), where the agent aims to learn the optimal policy based
on the data collected by a behavior policy, has attracted increasing attention in recent years …

The exploration-exploitation dilemma has been a central challenge in reinforcement
learning (RL) with complex model classes. In this paper, we propose a new algorithm …