arXiv Machine Learning June 4, 2026 By Jianlu Shen, Fu Feng, Yucheng Xie, Jiaqi Lv, Xin Geng

Breaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform

Read the original on arXiv Machine Learning →

arXiv:2603. 07523v3 Announce Type: replace Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which restricts flexible reuse across models of varying scales.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 5

GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models

arXiv:2506. 11042v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a resource-efficient strategy for adapting Pretrained Foundation Models (PFMs) by learning a small number of task-specific updates $\Delta W$.

By Guangning Xu, Baoquan Zhang, Michael. K. Ng

llms nlp fine-tuning benchmarks

arXiv AI

Jun 18

From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging

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.

By Stefan Horoi, Guy Wolf, Eugene Belilovsky, Gintare Karolina Dziugaite

fine-tuning

arXiv AI

Aug 12

Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?

arXiv:2603. 02462v2 Announce Type: replace-cross Abstract: A key challenge in developing unified neural solvers for combinatorial optimization (CO) is the efficient generalization of models from a given set of tasks to new tasks unseen during initial training.

By Semih Cant\"urk, Thomas Sabourin, Frederik Wenkel, Michael Perlmutter, Guy Wolf

fine-tuning

arXiv Machine Learning

Jun 18

PACT: Preserving Anchored Cores in Task-vectors for Model Merging

arXiv:2606. 18627v1 Announce Type: new Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a single multi-task model.

By Ningyuan Shi, Zhipeng Zhou, Hao Wang, Chunyan Miao, Peilin Zhao

fine-tuning benchmarks

arXiv Machine Learning

Jun 16

KeepLoRA++: Continual Learning with Layer-Scaled Residual Gradient Adaptation

arXiv:2606. 16256v1 Announce Type: cross Abstract: Continual learning for pre-trained vision-language models requires balancing three competing objectives: retaining pre-trained knowledge, preserving knowledge from a sequence of learned tasks, and maintaining the plasticity to acquire new knowledge.

By Mao-Lin Luo, Yi-Lin Zhang, Zi-Hao Zhou, Yankun Hong, Xialiang Tong, Mingxuan Yuan, Tong Wei, Min-Ling Zhang

llms computer-vision nlp fine-tuning multimodal

arXiv AI

Jun 11

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning

arXiv:2510. 04567v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundational Models (GFMs).

By Weishuo Ma, Yanbo Wang, Xiyuan Wang, Lei Zou, Muhan Zhang

llms

Related stories

GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models

From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging

Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?

PACT: Preserving Anchored Cores in Task-vectors for Model Merging

KeepLoRA++: Continual Learning with Layer-Scaled Residual Gradient Adaptation

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning