$\alpha$Transfer: Coefficient Transfer for Efficient Model Merging
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2608. 12842v1 Announce Type: new Abstract: Model merging has recently attracted significant attention as a promising paradigm for constructing unified multi-task models without requiring additional retraining.
arXiv:2506. 14126v2 Announce Type: replace-cross Abstract: Modern deep learning is increasingly characterized by the use of open-weight foundation models that can be fine-tuned on specialized datasets.
The paper introduces Riemannian–Lorentz Parameter Fusion (RLPF), a method for merging a Vision Transformer and a state‑space model without gradient descent. RLPF aligns parameter groups by semantic role, projects them onto a common coordinate system, lifts selected coordinates to the Lorentz hyperboloid, computes a regularized geodesic barycenter, and decodes the result back into the two branches, with a learned gate combining their logits. The resulting fine‑tuned system achieves 82.37 % on CIFAR‑10, 75.04 % on Oxford‑IIIT Pet, and 78.58 % top‑1 accuracy on ImageNet‑1K, surpassing the best‑parent accuracies of 76.54 %, 71.42 %, and 76.42 % respectively.
arXiv:2509. 02555v2 Announce Type: replace-cross Abstract: Model merging techniques aim to integrate the abilities of multiple models into a single model.
arXiv:2607. 18026v1 Announce Type: new Abstract: Can large language models with substantially different parameter spaces be merged by direct weighted averaging, without training or semantic alignment?
arXiv:2608. 04215v1 Announce Type: cross Abstract: The growing diversity of code clone types, from syntactic copies to cross-language semantic clones to AI-generated duplicates, has created a fragmentation crisis in clone detection.