15 citations · 60 across the 31 of their papers we have counts for
6 papers · 1 filter
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…
Chain-of-Thought Enhanced Shallow Transformers for Wireless Symbol Detection
Li Fan, Peng Wang, Jing Yang +1
Transformers have shown potential in solving wireless communication problems, particularly via in-context learning (ICL), where models adapt to new tasks through prompts without re…
A Shared Low-Rank Adaptation Approach to Personalized RLHF
Renpu Liu, Peng Wang, Donghao Li +2
Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal technique for aligning artificial intelligence systems with human values, achieving remarkable success in…
Decision Feedback In-Context Learning for Wireless Symbol Detection
Li Fan, Wei Shen, Jing Yang +1
Pre-trained Transformers, through in-context learning (ICL), have demonstrated exceptional capabilities to adapt to new tasks using example prompts without model update. Transforme…
Average Reward Reinforcement Learning for Wireless Radio Resource Management
Kun Yang, Jing Yang, Cong Shen
In this paper, we address a crucial but often overlooked issue in applying reinforcement learning (RL) to radio resource management (RRM) in wireless communications: the mismatch b…