Parameter-Efficient Fine-Tuning

Also known as PEFT

Parameter-efficient fine-tuning studies how to adapt pretrained models while training only a small subset of weights or adding lightweight modules, reducing resource costs and sometimes helping preserve existing capabilities. Common approaches include low-rank updates such as LoRA and quantized QLoRA, adapters and soft prompts, selective layer or rank tuning, and sparse or routed experts. Methods are evaluated on language benchmarks such as GLUE and mathematical reasoning, as well as vision, speech, multimodal, and continual-learning tasks, often with limited data or hardware. A central tension is that fewer trainable parameters do not necessarily mean lower memory use or faster training, and accuracy, robustness, and retention can vary with the model, task, and placement of updates.

Latest papers

610 papers75 papers in the last four weeks, up 12% on the four weeks before. 0.4% of all new papers.

FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data
Aug 6, 2025Thibaut Thonet, Germán Kruszewski, Jos Rozen +2LLM Personalization Personalized Language Models
1NAVER Labs Europe · 2Independent Researcher
NEARL: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding
Aug 6, 2025Zelin Peng, Yichen Zhao, Yu Huang +5Cross-Modal Learning VLM Adaptation
MoE Key Lab of Artificial Intelligence, AI Institute, School of Computer Science, Shanghai Jiao Tong University · Department of Radiology, The First Affiliated Hospital, School of Medicine, Zhejiang University · Mohamed bin Zayed University of Artificial Intelligence +1
PLoRA: Efficient Concurrent LoRA Training for Large Language Models
Aug 4, 2025Minghao Yan, Zhuang Wang, Zhen Jia +2LLM Fine-Tuning Efficient Language Model Training
University of Wisconsin-Madison · Amazon Web Services
WaRA: Wavelet Low-Rank Adaptation for Medical Image Classification
Jun 25, 2025Moein Heidari, Yijin Huang, Yasamin Medghalchi +3Vision Foundation Model Adaptation Low-Rank Adaptation
School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada · Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China · Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada +2
TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree
Jun 12, 2025Yu-Yang Qian, Yuan-Ze Xu, Zhen-Yu Zhang +2Continual Learning for LLMs Low-Rank Adaptation
National Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China · RIKEN AIP, Tokyo, Japan
MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning
Jun 10, 2025Javier Lopez-Piqueres, Pranav Deshpande, Archan Ray +3Transformer Tensor Decomposition
Global Technology Applied Research, JPMorgan Chase, New York, NY 10001, USA
COLORA: Efficient Fine-Tuning for Convolutional Models with a Study Case on Optical Coherence Tomography Image Classification
May 23, 2025Mariano Rivera, Angello HoyosLow-Rank Adaptation Convolutional Neural Networks
Centro de Investigación en Matemáticas, A.C. (CIMAT) Guanajuato, Guanajuato 36023, México
Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting
May 22, 2025Peyman Baghershahi, Sourav MedyaSource-Free Domain Adaptation Unsupervised Domain Adaptation
Department of Computer Science University of Illinois Chicago
BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language Models
Aug 8, 2024Yupeng Chang, Yi Chang, Yuan WuSocial Bias in Language Models Low-Rank Adaptation
School of Artificial Intelligence, Jilin University · Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University · International Center of Future Science, Jilin University
Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models
Jul 28, 2024Jifeng Wang, Kaouther Messaoud, Yuejiang Liu +2Parameter-Efficient Fine-Tuning Adapter Tuning
University of Bonn, Bonn, Germany · VITA Laboratory, EPFL, Lausanne, Switzerland · Stanford University, Stanford, USA

Aug 6, 2025J/K move · Enter open · S save

FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data

Thibaut Thonet, Germán Kruszewski, Jos Rozen, Pierre Erbacher+1

1NAVER Labs Europe · 2Independent Researcher

LLM Personalization Personalized Language Models Personalized Language Model Alignment Preference Alignment Parameter-Efficient Fine-Tuning

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LLM-powered conversational assistants are often deployed in a one-size-fits-all manner, which fails to accommodate individual user preferences. Recently, LLM personalization -- tailoring models to align with specific user preferences -- has gained increasing attention as a way to bridge this gap. In this work, we specifically focus on a practical yet challenging setting where only a small set of preference annotations can be collected per user -- a problem we define as Personalized Preference Alignment with Limited Data (PPALLI). To support research in this area, we introduce two datasets -- DnD and ELIP -- and benchmark a variety of alignment techniques on them. We further propose FaST, a highly parameter-efficient approach that leverages high-level features automatically discovered from the data, achieving the best overall performance.