6 papers
A Brief Overview: On-Policy Self-Distillation In Large Language Models
Fangming Cui, Sunan Li, Jiahong Li
On-Policy Self-Distillation (OPSD) is a unified learning framework in which a single large language model acts simultaneously as both teacher and student. Unlike conventional knowl…
Rethinking Agentic Reinforcement Learning In Large Language Models
Fangming Cui, Ruixiao Zhu, Cheng Fang +2
Reinforcement Learning (RL) has traditionally focused on training specialized agents to optimize predefined reward functions within narrowly defined environments. However, the adve…
Enhancing Target-unspecific Tasks through a Features Matrix
Fangming Cui, Yonggang Zhang, Xuan Wang +2
Recent developments in prompt learning of large Vision-Language Models (VLMs) have significantly improved performance in target-specific tasks. However, these prompting methods oft…
A Similarity Paradigm Through Textual Regularization Without Forgetting
Fangming Cui, Jan Fong, Rongfei Zeng +2
Prompt learning has emerged as a promising method for adapting pre-trained visual-language models (VLMs) to a range of downstream tasks. While optimizing the context can be effecti…
Generalizable Prompt Learning of CLIP: A Brief Overview
Fangming Cui, Yonggang Zhang, Xuan Wang +2
Existing vision-language models (VLMs) such as CLIP have showcased an impressive capability to generalize well across various downstream tasks. These models leverage the synergy be…
Advancing Prompt Learning through an External Layer
Fangming Cui, Xun Yang, Chao Wu +2
Prompt learning represents a promising method for adapting pre-trained vision-language models (VLMs) to various downstream tasks by learning a set of text embeddings. One challenge…