信息通信技术与政策2026，Vol.52Issue(2)：44-52,9.DOI:10.12267/j.issn.2096-5931.2026.02.007
基于分布式算力互联的大模型后训练成本优化技术综述
A review of post-training cost optimization technology for large language models based on distributed computing optimization
宁柯宇 1马飞 2李哲 2董晓慧2
作者信息
- 1. 电信科学技术研究院,北京 100191
- 2. 中国信息通信研究院云计算与数字化研究所,北京 100191
- 折叠
摘要
Abstract
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.关键词
算力互联网/大语言模型/后训练/成本优化Key words
internet of computing/large language models/post-training/cost optimization分类
信息技术与安全科学引用本文复制引用
宁柯宇,马飞,李哲,董晓慧..基于分布式算力互联的大模型后训练成本优化技术综述[J].信息通信技术与政策,2026,52(2):44-52,9.
