arXiv Machine Learning
Jul 3

SCAPE: Accurate and Efficient LLM Training with Extreme Sparse Communication

arXiv:2607. 01678v1 Announce Type: new Abstract: Communication increasingly dominates the cost of Large Language Model (LLM) pre-training, especially under data-parallel and sharded training schemes, where gradient synchronization and parameter reconstruction overhead increase with model size and system scale.

By Mingkai Zheng, Junlin Chen, Haotian Xie, Zhao Zhang
arXiv AI
Aug 5

Efficient Knowledge Distillation for LLMs: Offline Top-K Logits and a Fused Chunked KL Loss

arXiv:2608. 03796v1 Announce Type: cross Abstract: Small language models are often the only option for deployment under tight latency, cost, and on-premises constraints, but they are rarely trained from scratch: a compressed model is usually recovered through knowledge distillation (KD).

By Bakbergen Ryskulov, Iker Garc\'ia-Ferrero, David Montero, David Jansen, Ali Hashemi, Jezabel R. Garcia, Antonio Tiene, Rom\'an Or\'us