15 citations · 86 across the 29 of their papers we have counts for
13 papers · 1 filter
Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits
Donghao Li, Chengshuai Shi, Weijuan Ou +2
Prompt engineering has become central to eliciting the capabilities of large language models (LLMs). At its core lies prompt selection -- efficiently identifying the most effective…
-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses
Di Wu, Chengshuai Shi, Jing Yang +1
Reinforcement Learning from Human Feedback (RLHF) has become a cornerstone technique for post-training large language models. While most existing approaches rely on the reverse KL-…
Greedy Sampling Is Provably Efficient for RLHF
Di Wu, Chengshuai Shi, Jing Yang +1
Reinforcement Learning from Human Feedback (RLHF) has emerged as a key technique for post-training large language models. Despite its empirical success, the theoretical understandi…
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis
Ruiquan Huang, Donghao Li, Chengshuai Shi +2
This paper investigates a hybrid learning framework for reinforcement learning (RL) in which the agent can leverage both an offline dataset and online interactions to learn the opt…
Federated Representation Learning in the Under-Parameterized Regime
Renpu Liu, Cong Shen, Jing Yang
Federated representation learning (FRL) is a popular personalized federated learning (FL) framework where clients work together to train a common representation while retaining the…
Federated Linear Bandits with Finite Adversarial Actions
Li Fan, Ruida Zhou, Chao Tian +1
We study a federated linear bandits model, where clients communicate with a central server to solve a linear contextual bandits problem with finite adversarial action sets that…