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cs.CL2024
Refining Packing and Shuffling Strategies for Enhanced Performance in Generative Language Models
Yanbing Chen, Ruilin Wang, Zihao Yang +2
Packing and shuffling tokens is a common practice in training auto-regressive language models (LMs) to prevent overfitting and improve efficiency. Typically documents are concatena…
cs.LG2024★ 1 cited
Towards Faster Training of Diffusion Models: An Inspiration of A Consistency Phenomenon
Tianshuo Xu, Peng Mi, Ruilin Wang +1
Diffusion models (DMs) are a powerful generative framework that have attracted significant attention in recent years. However, the high computational cost of training DMs limits th…