Conventional machine learning studies generally assume close-environment scenarios
where important factors of the learning process hold invariant. With the great success of …

Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Large language models (LLMs)(Anil et al., 2023; Glaese et al., 2022; OpenAI, 2023; Ouyang
et al., 2022) have made remarkable strides in enhancing natural language understanding …

Aggregated predictors are obtained by making a set of basic predictors vote according to
some weights, that is, to some probability distribution. Randomized predictors are obtained …

This paper studies post-training large language models (LLMs) using preference feedback
from a powerful oracle to help a model iteratively improve over itself. The typical approach …

A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …

Abstract We propose Adversarially Trained Actor Critic (ATAC), a new model-free algorithm
for offline reinforcement learning (RL) under insufficient data coverage, based on the …

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

We consider nonconvex-concave minimax problems, $\min_ {\mathbf {x}}\max_ {\mathbf
{y}\in\mathcal {Y}} f (\mathbf {x},\mathbf {y}) $, where $ f $ is nonconvex in $\mathbf {x} $ but …

Policy gradient methods are among the most effective methods in challenging reinforcement
learning problems with large state and/or action spaces. However, little is known about even …