21 citations · 22 across the 7 of their papers we have counts for
7 papers
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Ang Li, Ben Liu, Bin Han +215
Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…
AntBatchInfer: Elastic Batch Inference in the Kubernetes Cluster
Siyuan Li, Youshao Xiao, Fanzhuang Meng +4
Offline batch inference is a common task in the industry for deep learning applications, but it can be challenging to ensure stability and performance when dealing with large amoun…
AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes
Youshao Xiao, Lin Ju, Zhenglei Zhou +8
Many distributed training techniques like Parameter Server and AllReduce have been proposed to take advantage of the increasingly large data and rich features. However, stragglers…
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs
Zhongshu Zhu, Bin Jing, Xiaopei Wan +3
As a powerful tool for modeling graph data, Graph Neural Networks (GNNs) have received increasing attention in both academia and industry. Nevertheless, it is notoriously difficult…
A Comprehensive Study of Knowledge Editing for Large Language Models
Ningyu Zhang, Yunzhi Yao, Bozhong Tian +19
Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies…
An Adaptive Placement and Parallelism Framework for Accelerating RLHF Training
Youshao Xiao, Zhenglei Zhou, Fagui Mao +6
Recently, ChatGPT or InstructGPT like large language models (LLM) has made a significant impact in the AI world. Many works have attempted to reproduce the complex InstructGPT's tr…
Rethinking Memory and Communication Cost for Efficient Large Language Model Training
Chan Wu, Hanxiao Zhang, Lin Ju +8
Recently, various distributed strategies for large language model training have been proposed. However, these methods provided limited solutions for the trade-off between memory co…