295 citations · 405 across the 47 of their papers we have counts for
4 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…
From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning
Zihan Chen, Song Wang, Xingbo Fu +4
The capability of in-context learning (ICL) enables large language models (LLMs) to perform novel tasks without parameter updates by conditioning on a few input-output examples. Ho…
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…
On the Convergence Analysis of Muon
Wei Shen, Ruichuan Huang, Minhui Huang +2
The majority of parameters in neural networks are naturally represented as matrices. However, most commonly used optimizers treat these matrix parameters as flattened vectors durin…