2 citations · 4 across the 7 of their papers we have counts for
8 papers
Accelerating RL for LLM Reasoning with Optimal Advantage Regression
Kianté Brantley, Mingyu Chen, Zhaolin Gao +4
Reinforcement learning (RL) has emerged as a powerful tool for fine-tuning large language models (LLMs) to improve complex reasoning abilities. However, state-of-the-art policy opt…
Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF
Zhaolin Gao, Wenhao Zhan, Jonathan D. Chang +4
Large Language Models (LLMs) have achieved remarkable success at tasks like summarization that involve a single turn of interaction. However, they can still struggle with multi-tur…
Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank
Wenhao Zhan, Scott Fujimoto, Zheqing Zhu +3
We study the problem of learning an approximate equilibrium in the offline multi-agent reinforcement learning (MARL) setting. We introduce a structural assumption -- the interactio…
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization
Audrey Huang, Wenhao Zhan, Tengyang Xie +4
Language model alignment methods such as reinforcement learning from human feedback (RLHF) have led to impressive advances in language model capabilities, but are limited by a wide…
Dataset Reset Policy Optimization for RLHF
Jonathan D. Chang, Wenhao Zhan, Owen Oertell +4
Reinforcement Learning (RL) from Human Preference-based feedback is a popular paradigm for fine-tuning generative models, which has produced impressive models such as GPT-4 and Cla…
REBEL: Reinforcement Learning via Regressing Relative Rewards
Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan +7
While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) application…
Optimal Multi-Distribution Learning
Zihan Zhang, Wenhao Zhan, Yuxin Chen +2
Multi-distribution learning (MDL), which seeks to learn a shared model that minimizes the worst-case risk across distinct data distributions, has emerged as a unified framework…
Provably Efficient CVaR RL in Low-rank MDPs
Yulai Zhao, Wenhao Zhan, Xiaoyan Hu +4
We study risk-sensitive Reinforcement Learning (RL), where we aim to maximize the Conditional Value at Risk (CVaR) with a fixed risk tolerance . Prior theoretical work studying…