147 citations · 1k across the 56 of their papers we have counts for
3 papers · 1 filter
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning
Zhonghua Jiang, Jimin Xu, Shengyu Zhang +5
Federated learning (FL) is a promising technology for data privacy and distributed optimization, but it suffers from data imbalance and heterogeneity among clients. Existing FL met…
Reinforcement Learning Enhanced LLMs: A Survey
Shuhe Wang, Shengyu Zhang, Jie Zhang +7
Reinforcement learning (RL) enhanced large language models (LLMs), particularly exemplified by DeepSeek-R1, have exhibited outstanding performance. Despite the effectiveness in imp…
Packing Analysis: Packing Is More Appropriate for Large Models or Datasets in Supervised Fine-tuning
Shuhe Wang, Guoyin Wang, Yizhong Wang +3
Packing, initially utilized in the pre-training phase, is an optimization technique designed to maximize hardware resource efficiency by combining different training sequences to f…