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cs.LG2026
Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs
Li Shen, Xiaolei Hao, Qinglun Li +3
One-Shot Federated Learning, where a central server learns a global model in a single communication round, has emerged as a promising paradigm. However, under extremely non-IID set…
cs.LG2026
PACE: Parameter Change for Unsupervised Environment Design
Fang Yuan, Quanjun Yin, Siqi Shen +5
Unsupervised Environment Design (UED) offers a promising paradigm for improving reinforcement learning generalization by adaptively shaping training environments, but it requires r…
cs.LG2026
A Unified Generalization Framework for Model Merging: Trade-offs, Non-Linearity, and Scaling Laws
Qinglun Li, Anke Tang, Miao Zhang +3
Model merging efficiently aggregates capabilities from multiple fine-tuned models into a single one, operating purely in parameter space without original data or expensive re-compu…