UVA RL Meetup

This weekly meetup is organized by Shangtong Zhang and Chen-Yu Wei for UVA RL folks to share interesting RL papers.

Spring 2025

Date	Presenter	Paper or Topic
Feb 7	Zixuan Xie	Analytic-DPM: An Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models Tutorial on Diffusion Models for Imaging and Vision
Feb 14	Xinyu Liu	Decoupled Functional Central Limit Theorems for Two-Time-Scale Stochastic Approximation
Feb 21	Amir Moeini	Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context
Feb 28 (AAAI)
Mar 7	Braham Snyder	Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation
Mar 14 (Spring Break)
Mar 21	Haolin Liu	Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF
Mar 28	Amin Davoodabadi (cancelled)	An Information-Theoretic Perspective on Intrinsic Motivation in Reinforcement Learning
Apr 4	Jiuqi Wang	Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?
Apr 11	Dylan Foster (remote)	Revisiting the Foundations of Imitation Learning
Apr 18	Xiangyu Liu (remote)	Provable Partially Observable Reinforcement Learning with Privileged Information
Apr 25 (ICLR)

Date	Presenter	Paper
Sep 27	Shangtong Zhang	The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
Oct 11	Chen-Yu Wei	Equivalence Between Policy Gradients and Soft Q-Learning
Oct 25	Shuze Liu	Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Nov 8	Ethan Blaser	Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining
Nov 22	Haolin Liu	Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Χ²-Preference Optimization
Dec 6	Jiuqi Wang	Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning Denoising Diffusion Probabilistic Models Understanding Diffusion Models: A Unified Perspective