1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Towards On-Policy SFT: Distribution Discriminant Theory and its Applications in LLM Training
Miaosen Zhang, Yishan Liu, Shuxia Lin +8
Supervised fine-tuning (SFT) is computationally efficient but often yields inferior generalization compared to reinforcement learning (RL). This gap is primarily driven by RL's use…
cs.LG2025★ 1 cited
STHFL: Spatio-Temporal Heterogeneous Federated Learning
Shunxin Guo, Hongsong Wang, Shuxia Lin +2
Federated learning is a new framework that protects data privacy and allows multiple devices to cooperate in training machine learning models. Previous studies have proposed multip…
cs.LG2024
Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization
Shunxin Guo, Hongsong Wang, Shuxia Lin +2
Federated learning is an efficient framework designed to facilitate collaborative model training across multiple distributed devices while preserving user data privacy. A significa…