1 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
Reassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning
Yongcan Yu, Lingxiao He, Shuo Lu +10
Recent advances in vision-language models (VLMs) reasoning have been largely attributed to the rise of reinforcement Learning (RL), which has shifted the community's focus away fro…
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging
Kuangpu Guo, Aijing Yu, Jian Liang +4
Model merging has emerged as a promising paradigm for enabling multi-task capabilities without additional training. However, traditional basic merging methods often experience perf…
Cooperative Pseudo Labeling for Unsupervised Federated Classification
Kuangpu Guo, Lijun Sheng, Yongcan Yu +3
Unsupervised Federated Learning (UFL) aims to collaboratively train a global model across distributed clients without sharing data or accessing label information. Previous UFL work…
Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion
Yuguang Zhang, Kuangpu Guo, Zhihe Lu +2
Federated learning (FL) enables collaborative model training across decentralized clients without sharing local data, but is challenged by heterogeneity in data, computation, and c…