arXiv subjects

Tianbing Xu

Publications and source records attributed to Tianbing Xu.

7 recordsLinked to original sources

arXiv2026

An Expectation-Maximization Perspective on Reinforcement Learning for LLM Reasoning

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} and DeepSeek-R1~\cite{r1}. However, widely used algorithms such as PPO~\cite{ppo} and GRPO~\cite{grpo} rely on likelihood-ratio correction and heuristic clipping, which increase algorithmic complexity and may introduce additional variance and optimization bias. We present \textbf{EM Policy Gradient (EMPG)}, a simple and principled framework that formulates reinforcement learning over reasoning trajectories as an Expectation-Maximization (EM) problem~\cite{em, dempster1977em}. The E-step constructs a reward-weighted distribution over rationale trajectories, while the M-step improves the language model by maximizing their weighted log-likelihood. This formulation leads to a practical off-policy policy-gradient algorithm that directly optimizes the EM surrogate objective without trajectory-level importance weighting or likelihood-ratio clipping. Moreover, under exact E- and improving M-steps, EMPG guarantees monotonic improvement of the expected-reward objective. Experiments on GSM8K and MATH Hard with multiple Qwen2.5 base models show that EMPG achieves performance comparable to or better than GRPO while using a substantially simpler optimization procedure. EMPG also produces more concise reasoning trajectories and exhibits structured reasoning behaviors, including subproblem decomposition, self-verification, reflection, and backtracking. These results suggest that the EM perspective provides an effective theoretical and algorithmic foundation for reinforcement learning of reasoning language models.

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arXiv2019

WALL-E: An Efficient Reinforcement Learning Research Framework

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 bottleneck. Thus, it is necessary to speed up the rollout generation time with multi-process architecture support. Our work, dubbed WALL-E, utilizes multiple rollout samplers running in parallel to rapidly generate experience. Due to our parallel samplers, we experience not only faster convergence times, but also higher average reward thresholds. For example, on the MuJoCo HalfCheetah-v2 task, with $N = 10$ parallel sampler processes, we are able to achieve much higher average return than those from using only a single process architecture.

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arXiv2018

Stochastic Variance Reduction for Policy Gradient Estimation

Recent advances in policy gradient methods and deep learning have demonstrated their applicability for complex reinforcement learning problems. However, the variance of the performance gradient estimates obtained from the simulation is often excessive, leading to poor sample efficiency. In this paper, we apply the stochastic variance reduced gradient descent (SVRG) to model-free policy gradient to significantly improve the sample-efficiency. The SVRG estimation is incorporated into a trust-region Newton conjugate gradient framework for the policy optimization. On several Mujoco tasks, our method achieves significantly better performance compared to the state-of-the-art model-free policy gradient methods in robotic continuous control such as trust region policy optimization (TRPO)

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arXiv2018

Learning to Explore with Meta-Policy Gradient

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. Existing exploration methods are mostly based on adding noise to the on-going actor policy and can only explore \emph{local} regions close to what the actor policy dictates. In this work, we develop a simple meta-policy gradient algorithm that allows us to adaptively learn the exploration policy in DDPG. Our algorithm allows us to train flexible exploration behaviors that are independent of the actor policy, yielding a \emph{global exploration} that significantly speeds up the learning process. With an extensive study, we show that our method significantly improves the sample-efficiency of DDPG on a variety of reinforcement learning tasks.

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arXiv2018

Variational Inference for Policy Gradient

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 distribution by \textit{explicitly} minimizing the KL divergence to match the target distribution in an amortize fashion. Consequently, we applied this varational inference technique into vanilla policy gradient, TRPO and PPO with Bayesian Neural Network parameterizations for reinforcement learning problems.

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arXiv2013

Online Classification Using a Voted RDA Method

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) method, but only on the subsequence of training examples where a classification error is made. We derive a bound on the number of mistakes made by this method on the training set, as well as its generalization error rate. We also introduce the concept of relative strength of regularization, and show how it affects the mistake bound and generalization performance. We experimented with the method using $\ell_1$ regularization on a large-scale natural language processing task, and obtained state-of-the-art classification performance with fairly sparse models.

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arXiv2013

Thompson Sampling in Dynamic Systems for Contextual Bandit Problems

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 the posterior distributions based on Laplace Approximation. For the context bandit problems, Thompson Sampling is adopted based on the underlying posterior distributions of the parameters. More specifically, we introduce the discount decays on the previous samples impact and analyze the different decay rates with the underlying sample dynamics. Consequently, the exploration and exploitation is adaptively tradeoff according to the dynamics in the system.

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