3 papers
cs.LG2026
pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training
Wenzheng Zhang, Bingzheng Liu, Yang Hu +3
Quantization-Aware Training from scratch has emerged as a promising approach for building efficient large language models (LLMs) with extremely low-bit weights (sub 2-bit), which c…
cs.SE2026
The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes
Redacted by arXiv
This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…
cs.DC2026
Collective Communication for 100k+ GPUs
Min Si, Pavan Balaji, Yongzhou Chen +36
The increasing scale of large language models (LLMs) necessitates highly efficient collective communication frameworks, particularly as training workloads extend to hundreds of tho…