6 papers
Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging
Chenrui Wu, Zexi Li, Jiajun Bu +2
Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a powerful post-training paradigm that surpasses Supervised Fine-Tuning (SFT) in eliciting reasoning intelligenc…
Improving Model Fusion by Training-time Neuron Alignment with Fixed Neuron Anchors
Zexi Li, Zhiqi Li, Jie Lin +5
Model fusion aims to integrate several deep neural network (DNN) models' knowledge into one by fusing parameters, and it has promising applications, such as improving the generaliz…
FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning
Tao Shen, Zexi Li, Didi Zhu +3
Federated learning (FL) is a machine learning paradigm that allows multiple clients to collaboratively train a shared model without exposing their private data. Data heterogeneity…
Text2Weight: Bridging Natural Language and Neural Network Weight Spaces
Bowen Tian, Wenshuo Chen, Zexi Li +3
How far are we really from automatically generating neural networks? While neural network weight generation shows promise, current approaches struggle with generalization to unseen…
FedGuCci: Making Local Models More Connected in Landscape for Federated Learning
Zexi Li, Jie Lin, Zhiqi Li +5
Federated learning (FL) involves multiple heterogeneous clients collaboratively training a global model via iterative local updates and model fusion. The generalization of FL's glo…
Merging LoRAs like Playing LEGO: Pushing the Modularity of LoRA to Extremes Through Rank-Wise Clustering
Ziyu Zhao, Tao Shen, Didi Zhu +5
Low-Rank Adaptation (LoRA) has emerged as a popular technique for fine-tuning large language models (LLMs) to various domains due to its modular design and widespread availability…