4 citations · 6 across the 11 of their papers we have counts for
4 papers · 1 filter
Towards Simple and Provable Parameter-Free Adaptive Gradient Methods
Yuanzhe Tao, Yifeng Liu, Huizhuo Yuan +3
Optimization algorithms such as AdaGrad and Adam have significantly advanced the training of deep models by dynamically adjusting the learning rate during the optimization process.…
MARS: Unleashing the Power of Variance Reduction for Training Large Models
Huizhuo Yuan, Yifeng Liu, Shuang Wu +2
Training deep neural networks--and more recently, large models demands efficient and scalable optimizers. Adaptive gradient algorithms like Adam, AdamW, and their variants have bee…
Accelerated Preference Optimization for Large Language Model Alignment
Jiafan He, Huizhuo Yuan, Quanquan Gu
Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal tool for aligning large language models (LLMs) with human preferences. Direct Preference Optimization (DP…
Beyond Bradley-Terry Models: A General Preference Model for Language Model Alignment
Yifan Zhang, Ge Zhang, Yue Wu +2
Modeling human preferences is crucial for aligning foundation models with human values. Traditional reward modeling methods, such as the Bradley-Terry (BT) reward model, fall short…