6 papers · 1 filter
Learning the Pareto Frontier of Predictive Models under Distribution Shift
Yiming Dong, Jiwei Zhao, Yang Young Lu
Modern machine learning pipelines increasingly rely on reusing pretrained and foundation models across downstream tasks. These pretrained models can differ not only in performance…
Improving Model Representation and Reducing KV Cache via Skip Connections with First Value Heads
Zhoutong Wu, Yuan Zhang, Yiming Dong +4
Transformer models have driven breakthroughs across various language tasks by their strong capability to learn rich contextual representations. Scaling them to improve representati…
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…
Stepsize anything: A unified learning rate schedule for budgeted-iteration training
Anda Tang, Yiming Dong, Yutao Zeng +2
The expanding computational costs and limited resources underscore the critical need for budgeted-iteration training, which aims to achieve optimal learning within predetermined it…
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…