基于分布式算力互联的大模型后训练成本优化技术综述OA

Amidst the rapid development of the Internet of computing,escalating computational costs during the post-training phase of large language models(LLMs)have become a critical bottleneck hindering widespread technology adoption.First,by systematically organizing and training cost optimization technology system,a comprehensive framework is constructed to reduce computational,storage,and data overheads,leveraging the cross-domain collaboration characteristics of the computing power internet.Second,the limitations of existing mainstream techniques are analyzed,and the evolution trends in this field are summarized to explore new directions for post-training cost optimization techniques of large models in distributed computing power interconnection environments.