On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters
Mind Lab（心智实验室）
Mind Lab, :, Vin Bo, Song Cao, Vic Cao, Andrew Chen, Kaijie Chen, Cleon Cheng, Steven Chiang, Kaixuan Fan, Hera Feng, Huan Feng, Arthur Fu, Jun Gao, Hongquan Gu… 展开作者
Mind Lab, :, Vin Bo, Song Cao, Vic Cao, Andrew Chen, Kaijie Chen, Cleon Cheng, Steven Chiang, Kaixuan Fan, Hera Feng, Huan Feng, Arthur Fu, Jun Gao, Hongquan Gu, Aaron Guan, Nolan Ho, Mutian Hong, Hailee Hou, Peixuan Hua, Charles Huang, Miles Jiang, Nora Jiang, Yuyi Jiang, Qiuyu Jin, Fancy Kong, Andrew Lei, Kyrie Lei, Alexy Li, Lucian Li, Ray Li, Theo Li, Wenhao Li, Zhihui Li, Allen Lin, Jiayi Lin, Kairus Liu, Kieran Liu, Logan Liu, Xiang Liu, Irvine Lu, Maeve Luo, Runze Lv, Pony Ma, Verity Niu, Anson Qiu, Vincent Wang, Rio Yang, Maxwell Yao, Carrie Ye, Regis Ye, Wenlin Ye, Josh Ying, Danney Zeng, Yuhan Zhan, Anya Zhang, Di Zhang, Ruijia Zhang, Shiyang Zhang, Sueky Zhang, Ya Zhang, Wei Zhao, Ada Zhou, Adrian Zhou, Yuhua Zhou, Xinyue Zhu, Murphy Zhuang
Parameter-efficient fine-tuning (PEFT) is usually treated as a cheaper alternative to full fine-tuning. We study a broader role: small trainable adapters as persistent local state on top of strong shared foundation models. In this framing, the base model provides shared competence while adapters carry instance-specific behavior such as preferences, skills, tool habits, and memory-like updates. We organize the problem around three scaling axes: Scale Up, where stronger shared priors make small local updates more useful; Scale Down, where we study how small adapters can be while remaining reliable; and Scale Out, where many persistent adapted instances coexist. MinT provides one infrastructure example for managing adapter identity, revision, provenance, evaluation, and serving residency. Together, the results suggest that PEFT can be a compact substrate for persistent personal models rather than only a budget substitute for full fine-tuning.
Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark
Nanjing University（南京大学）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Jiao Tong University（上海交通大学）；University of Bristol（布里斯托大学）；Shandong University（山东大学）；University of Sydney（悉尼大学）；University of Minnesota Twin Cities（明尼苏达大学双城分校）；The Pennsylvania State University（宾夕法尼亚州立大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；University of California at Merced（加州大学默塞德分校）
Resource-Efficient Personal Large Language Models Fine-Tuning with Collaborative Edge Computing
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Department of Information Engineering, The Chinese University of Hong Kong（香港中文大学信息工程系）；The Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（社会人工智能与机器人研究所（深圳））
LoRA Diffusion: Zero-Shot LoRA Synthesis for Diffusion Model Personalization
Leonardo AI Research Lab（莱昂纳多人工智能研究实验室）；Lucid Simulations, Corp.（Lucid Simulations公司）；Precigenetics Inc（Precigenetics公司）；SimpleBerry Research Lab（SimpleBerry研究实验室）；University of California, Merced（加州大学默塞德分校）
