ICML 2025

Surrogate Prompt Learning: Towards Efﬁcient and
Diverse Prompt Learning for Vision-Language Models
Liangchen Liu 1 Nannan Wang 1 Xi Yang 1 Xinbo Gao 1 Tongliang Liu 2
Abstract
Prompt learning is a cutting-edge parameter-
efﬁcient ﬁne-tuning technique for pre-trained
vision-language models (VLMs). Instead of learn-
ing a single text prompt, recent works have re-
vealed that learning diverse text prompts can ef-
fectively boost the performances on downstream
tasks, as the diverse prompted text features can
comprehensively depict the visual concepts from
different perspectives. However, diverse prompt
learning demands enormous computational re-
sources. This efﬁciency issue still remains un-
explored. To achieve efﬁcient and diverse prompt
learning, this paper proposes a novel Surro-
gate Prompt Learning (SurPL) framework. In-
stead of learning diverse text prompts, SurPL di-
rectly generates the desired prompted text fea-
tures via a lightweight Surrogate Feature Gen-
erator (SFG), thereby avoiding the complex gra-
dient computation procedure of conventional di-
verse prompt learning. Concretely, based on a
basic prompted text feature, SFG can directly
and efﬁciently generate diverse prompted fea-
tures according to different pre-deﬁned condi-
tional signals. Extensive experiments indicate
the effectiveness of the surrogate prompted text
features, and show compelling performances
and efﬁciency of SurPL on various benchmarks.
Code is available at https://github.com/
llcllc1997/SurPL.
1. Introduction
Recently, vision-language models (VLMs) (Radford et al.,
2021; Jia et al., 2021; Yao et al., 2021; Zhai et al., 2022)
have shown great capability in open-world visual under-
1Xidian University, Xi’an 710071, Shaanxi, China. 2The Uni-
versity of Sydney, Darlington, NSW 2008, Australia. Correspon-
dence to: Nannan Wang <nnwang@xidian.edu.cn>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the author(...