921 citations · 1k across the 30 of their papers we have counts for
25 papers · 1 filter
Pairwise Proximal Policy Optimization: Harnessing Relative Feedback for LLM Alignment
Tianhao Wu, Banghua Zhu, Ruoyu Zhang +3
Large Language Models (LLMs) can acquire extensive world knowledge through pre-training on large corpora. However, due to exposure to low-quality data, LLMs may exhibit harmful beh…
On Optimal Caching and Model Multiplexing for Large Model Inference
Banghua Zhu, Ying Sheng, Lianmin Zheng +3
Large Language Models (LLMs) and other large foundation models have achieved noteworthy success, but their size exacerbates existing resource consumption and latency challenges. In…
Doubly Robust Self-Training
Banghua Zhu, Mingyu Ding, Philip Jacobson +4
Self-training is an important technique for solving semi-supervised learning problems. It leverages unlabeled data by generating pseudo-labels and combining them with a limited lab…
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
Hanlin Zhu, Paria Rashidinejad, Jiantao Jiao
We propose A-Crab (Actor-Critic Regularized by Average Bellman error), a new practical algorithm for offline reinforcement learning (RL) in complex environments with insufficient d…
Online Learning in Stackelberg Games with an Omniscient Follower
Geng Zhao, Banghua Zhu, Jiantao Jiao +1
We study the problem of online learning in a two-player decentralized cooperative Stackelberg game. In each round, the leader first takes an action, followed by the follower who ta…
Principled Reinforcement Learning with Human Feedback from Pairwise or -wise Comparisons
Banghua Zhu, Jiantao Jiao, Michael I. Jordan
We provide a theoretical framework for Reinforcement Learning with Human Feedback (RLHF). Our analysis shows that when the true reward function is linear, the widely used maximum l…