63 citations · 137 across the 16 of their papers we have counts for
4 papers · 1 filter
Conda: Column-Normalized Adam for Training Large Language Models Faster
Junjie Wang, Pan Zhou, Yiming Dong +6
Large language models (LLMs) have demonstrated impressive generalization and emergent capabilities, yet their pre-training remains computationally expensive and sensitive to optimi…
On the Convergence Rate of AdamW Measured by Norm
Huan Li, Yiming Dong, Zhouchen Lin
As the default optimizer for training large language models, AdamW has achieved remarkable success in deep learning. However, its convergence behavior is not theoretically well-und…
Convergence Rate Analysis of LION
Yiming Dong, Huan Li, Zhouchen Lin
The LION (evoLved sIgn mOmeNtum) optimizer for deep neural network training was found by Google via program search, with the simple sign update yet showing impressive performance i…
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
Xingyu Xie, Pan Zhou, Huan Li +2
In deep learning, different kinds of deep networks typically need different optimizers, which have to be chosen after multiple trials, making the training process inefficient. To r…