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Parallelism Strategy Chaining for Fast Training Convergence
Minchul Kang, Changyong Shin, Younghun Go +4
Selecting a parallelism strategy - the configuration of data, tensor, and pipeline parallelism degrees together with micro- and global-batch sizes - largely determines the training…
Training Time Prediction for Mixed Precision-based Distributed Training
Minchul Kang, Changyong Shin, Jinwoo Jeong +5
Accurate prediction of training time in distributed deep learning is crucial for resource allocation, cost estimation, and job scheduling. We observe that the floating-point precis…
GPU Memory Prediction for Multimodal Model Training
Jinwoo Jeong, Minchul Kang, Younghun Go +5
As deep learning models in agentic AI systems grow in scale and complexity, GPU memory requirements increase and often exceed the available GPU memory capacity, so that out-of-memo…
On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning
Hojoon Lee, Koanho Lee, Dongyoon Hwang +3
Recently, unsupervised representation learning (URL) has improved the sample efficiency of Reinforcement Learning (RL) by pretraining a model from a large unlabeled dataset. The un…
JORLDY: a fully customizable open source framework for reinforcement learning
Kyushik Min, Hyunho Lee, Kwansu Shin +4
Recently, Reinforcement Learning (RL) has been actively researched in both academic and industrial fields. However, there exist only a few RL frameworks which are developed for res…