39 citations · 96 across the 14 of their papers we have counts for
14 papers
Faster WIND: Accelerating Iterative Best-of- Distillation for LLM Alignment
Tong Yang, Jincheng Mei, Hanjun Dai +5
Recent advances in aligning large language models with human preferences have corroborated the growing importance of best-of-N distillation (BOND). However, the iterative BOND algo…
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF
Shicong Cen, Jincheng Mei, Katayoon Goshvadi +6
Reinforcement learning from human feedback (RLHF) has demonstrated great promise in aligning large language models (LLMs) with human preference. Depending on the availability of pr…
Beyond Expectations: Learning with Stochastic Dominance Made Practical
Shicong Cen, Jincheng Mei, Hanjun Dai +3
Stochastic dominance serves as a general framework for modeling a broad spectrum of decision preferences under uncertainty, with risk aversion as one notable example, as it natural…
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning
Tong Yang, Shicong Cen, Yuting Wei +2
Federated reinforcement learning (RL) enables collaborative decision making of multiple distributed agents without sharing local data trajectories. In this work, we consider a mult…
Global Convergence of Policy Gradient Methods in Reinforcement Learning, Games and Control
Shicong Cen, Yuejie Chi
Policy gradient methods, where one searches for the policy of interest by maximizing the value functions using first-order information, become increasingly popular for sequential d…
Asynchronous Gradient Play in Zero-Sum Multi-agent Games
Ruicheng Ao, Shicong Cen, Yuejie Chi
Finding equilibria via gradient play in competitive multi-agent games has been attracting a growing amount of attention in recent years, with emphasis on designing efficient strate…
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games
Shicong Cen, Yuejie Chi, Simon S. Du +1
Multi-Agent Reinforcement Learning (MARL) -- where multiple agents learn to interact in a shared dynamic environment -- permeates across a wide range of critical applications. Whil…
Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization
Shicong Cen, Fan Chen, Yuejie Chi
A major challenge in multi-agent systems is that the system complexity grows dramatically with the number of agents as well as the size of their action spaces, which is typical in…
Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization
Shicong Cen, Yuting Wei, Yuejie Chi
This paper investigates the problem of computing the equilibrium of competitive games, which is often modeled as a constrained saddle-point optimization problem with probability si…
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
Wenhao Zhan, Shicong Cen, Baihe Huang +3
Policy optimization, which finds the desired policy by maximizing value functions via optimization techniques, lies at the heart of reinforcement learning (RL). In addition to valu…
Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization
Shicong Cen, Chen Cheng, Yuxin Chen +2
Natural policy gradient (NPG) methods are among the most widely used policy optimization algorithms in contemporary reinforcement learning. This class of methods is often applied i…
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction
Boyue Li, Shicong Cen, Yuxin Chen +1
There is growing interest in large-scale machine learning and optimization over decentralized networks, e.g. in the context of multi-agent learning and federated learning. Due to t…
Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data
Shicong Cen, Huishuai Zhang, Yuejie Chi +2
Stochastic variance reduced methods have gained a lot of interest recently for empirical risk minimization due to its appealing run time complexity. When the data size is large and…
A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization
Andre Milzarek, Xiantao Xiao, Shicong Cen +2
In this work, we present a globalized stochastic semismooth Newton method for solving stochastic optimization problems involving smooth nonconvex and nonsmooth convex terms in the…