24 citations · 33 across the 7 of their papers we have counts for
7 papers
An Expectation-Maximization Perspective on Reinforcement Learning for LLM Reasoning
Tianbing Xu
Reinforcement learning has emerged as a powerful approach for improving the reasoning capabilities of large language models, as demonstrated by systems such as OpenAI's O1~\cite{o1…
WALL-E: An Efficient Reinforcement Learning Research Framework
Tianbing Xu, Andrew Zhang, Liang Zhao
There are two halves to RL systems: experience collection time and policy learning time. For a large number of samples in rollouts, experience collection time is the major bottlene…
Learning to Explore with Meta-Policy Gradient
Tianbing Xu, Qiang Liu, Liang Zhao +1
The performance of off-policy learning, including deep Q-learning and deep deterministic policy gradient (DDPG), critically depends on the choice of the exploration policy. Existin…
Variational Inference for Policy Gradient
Tianbing Xu
Inspired by the seminal work on Stein Variational Inference and Stein Variational Policy Gradient, we derived a method to generate samples from the posterior variational parameter…
Stochastic Variance Reduction for Policy Gradient Estimation
Tianbing Xu, Qiang Liu, Jian Peng
Recent advances in policy gradient methods and deep learning have demonstrated their applicability for complex reinforcement learning problems. However, the variance of the perform…
Thompson Sampling in Dynamic Systems for Contextual Bandit Problems
Tianbing Xu, Yaming Yu, John Turner +1
We consider the multiarm bandit problems in the timevarying dynamic system for rich structural features. For the nonlinear dynamic model, we propose the approximate inference for t…
Online Classification Using a Voted RDA Method
Tianbing Xu, Jianfeng Gao, Lin Xiao +1
We propose a voted dual averaging method for online classification problems with explicit regularization. This method employs the update rule of the regularized dual averaging (RDA…