11 citations · 44 across the 18 of their papers we have counts for
4 papers · 1 filter
Generalized Gradient Norm Clipping & Non-Euclidean -Smoothness
Thomas Pethick, Wanyun Xie, Mete Erdogan +3
This work introduces a hybrid non-Euclidean optimization method which generalizes gradient norm clipping by combining steepest descent and conditional gradient approaches. The meth…
Layer-wise Quantization for Quantized Optimistic Dual Averaging
Anh Duc Nguyen, Ilia Markov, Frank Zhengqing Wu +4
Modern deep neural networks exhibit heterogeneity across numerous layers of various types such as residuals, multi-head attention, etc., due to varying structures (dimensions, acti…
Multi-Step Alignment as Markov Games: An Optimistic Online Gradient Descent Approach with Convergence Guarantees
Yongtao Wu, Luca Viano, Yihang Chen +4
Reinforcement Learning from Human Feedback (RLHF) has been highly successful in aligning large language models with human preferences. While prevalent methods like DPO have demonst…
Training Deep Learning Models with Norm-Constrained LMOs
Thomas Pethick, Wanyun Xie, Kimon Antonakopoulos +3
In this work, we study optimization methods that leverage the linear minimization oracle (LMO) over a norm-ball. We propose a new stochastic family of algorithms that uses the LMO…