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.

Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning
May 31, 2026Anushka Tiwari, Kaiyi JiStorage-Efficient Continual Learning Parameter-Efficient Fine-Tuning
Institute for Artificial Intelligence and Data Science, University at Buffalo, Buffalo, NY, USA · Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA
Finer Parameter Steps for Low-Rank PEFT: A Controlled Study with CP Tensor Adapters
May 29, 2026Xinjue Wang, Xiuheng Wang, Yejun Zhang +3Tensor Decomposition Adapter Tuning
Department of Information and Communications Engineering, Aalto University, 02150, Espoo, Finland · Université de Lorraine, CNRS, CRAN, France · Department of Computer Science, Aalto University, 02150, Espoo, Finland +1
CRMA: A Spectrally-Bounded Backbone for Modular Continual Fine-Tuning of LLMs
May 29, 2026Kiran Nayudu, Aswini Nutakki, Sai Vinay Naidu +1Continual Learning for LLMs Fine-Tuning
ModelBrew AI
Balanced LoRA: Removing Parameter Invariance to Accelerate Convergence
May 29, 2026Valérie Castin, Kimia Nadjahi, Pierre Ablin +1LLM Fine-Tuning Low-Rank Adaptation
1 ´Ecole Normale Sup´erieure PSL, Paris, France · 2CNRS · 3Apple, Paris, France.
TALON: Token-Aligned Lightweight Adapters for 6-DoF Spacecraft Pose Estimation
May 29, 2026Abid Ali, Nidhal Eddine Chenni, Arunkumar Rathinam +1Vision Foundation Model Adaptation Object Pose Estimation
TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning
May 29, 2026Xiaosong Han, Ke Chen, Xindi Dai +7Continual Learning for LLMs LLM Fine-Tuning
Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education · College of Computer Science and Technology, Jilin University Changchun, China · College of Software, Jilin University Changchun, China +3
Foundation-Preserving Optimization in Generalized Eigenspace
May 28, 2026Dongjun Kim, Adrian de Wynter, Huancheng Chen +2Low-Rank Adaptation Parameter-Efficient Fine-Tuning
The University of Texas at Austin · Microsoft · Microsoft AI +1
How LoRA Remembers? A Parametric Memory Law for LLM Finetuning
May 28, 2026Ziwen Xu, Haiwen Hong, Linsong Yu +4Memorization in Language Models LLM Fine-Tuning
Zhejiang University · Alibaba Group
NaRA: Noise-Aware LoRA for Parameter-Efficient Fine-Tuning of Diffusion LLMs
May 28, 2026Shuaidi Wang, Zhan Zhuang, Ruping Huang +1Low-Rank Adaptation Diffusion Language Models
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China · Department of Computer Science, City University of Hong Kong, Hong Kong, China · Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China
Leveraging Routing Dynamics in Mixture-of-Experts Models for Efficient Language Adaptation
May 28, 2026Aditi Khandelwal, Marius Mosbach, Verna Dankers +2Multilingual Language Models Continual Learning for LLMs
Mila – Quebec AI Institute & McGill University
FedSmoothLoRA: Toward Smoother and Faster Convergence in Federated Low-Rank Adaptation
May 28, 2026Zehao Wang, Guanglei Yang, Yihan Zeng +4Low-Rank Adaptation Federated Fine-Tuning
1Harbin Institute of Technology · 2Huawei Noah’s Ark Lab · University College Dublin
FoRA: Fisher-orthogonal Rank Adaptation for Parameter-Efficient Fine-Tuning
May 28, 2026Juneyoung Park, Seongbae Lee, Han-Sang Lee +6LLM Fine-Tuning Adapter Tuning
OptAI Inc · LG Uplus
Knowledge Offloading: Decomposing LLMs into Sparse Backbones and Memory Modules
May 27, 2026Karim Galliamov, Rochelle Choenni, Ivan TitovLLM Pruning LLM Compression
University of Amsterdam · University of Edinburgh
PEFT-Arena: Understanding Parameter-Efficient Finetuning from a Stability-Plasticity Perspective
May 27, 2026Yangyi Huang, Ruotian Peng, Zeju Qiu +4LLM Evaluation Fine-Tuning
The Chinese University of Hong Kong · Westlake University · MPI for Intelligent Systems
Janus-LoRA: A Balanced Low-Rank Adaptation for Continual Learning
May 27, 2026Cheng Chen, Pengpeng Zeng, Yuyu Guo +3Stability-Plasticity Dilemma Continual Learning
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China · School of Computer Science and Technology, Tongji University, Shanghai, China · Independent Researcher +1
PromptEmbedder: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting
May 27, 2026Yu-Che Tsai, Kuan-Yu Chen, Yuan-Hao Chen +4Text Embeddings Prompt Tuning
Department of Computer Science and Information Engineering, National Taiwan University · National Taiwan University AI Center of Research Excellence Taipei, Taiwan
Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning
May 27, 2026Seunghwan Keum, Alok WareyAutomotive Aerodynamics PDE Surrogate Modeling
General Motors Research and Development
SIGMA: Bridging Structural and Distributional Gaps for Vision Foundation Model Adaptation
May 27, 2026Lingyu Xiong, Jinjin Shi, Xuran Xu +3Vision Foundation Model Adaptation Dense Prediction
Xiaomi Corporation
CAREF: Calibration-Aware Regularization for Explanation Faithfulness Without Rationale Supervision
May 27, 2026Naphat Nithisopa, Teerapong PanboonyuenFaithfulness of Language Model Explanations LLM Interpretability
†MARSAIL · ‡Chulalongkorn University · §PBYAIL (Panboonyuen AI Lab)
Energy-Structured Low-Rank Adaptation for Continual Learning
May 26, 2026Longhua Li, Lei Qi, Qi Tian +1Continual Learning Low-Rank Adaptation
School of Computer Science and Engineering, Southeast University, Nanjing, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China · Huawei Technologies, Shenzhen, China
Evi-Steer: Learning to Steer Biomedical Vision-Language Models through Efficient and Generalizable Evidential Tuning
May 25, 2026Taha Koleilat, Hassan Rivaz, Yiming XiaoEvidential Deep Learning VLM Adaptation
Concordia University, Montreal, Canada
RotMoLE: Enhancing Mixture of Low-Rank Experts through Rotational Gating Mechanism
May 25, 2026Mengyang Sun, Maochuan Dou, Tao Feng +5LLM Fine-Tuning Low-Rank Adaptation
Tsinghua University · Beijing Information Science and Technology University · National University of Singapore +2
RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation
May 25, 2026Wenhui ChuVision Foundation Model Adaptation Robotic Manipulation
Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models
May 25, 2026Kaixiang Chen, Pengfei Fang, Hui XueCross-Modal Learning Cross-Modal Representation Learning
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China
Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts
May 24, 2026Xiangtian Ji, Yuxin Chen, Zhengzhou Cai +3Fine-Tuning LLM Interpretability
National University of Singapore · Beijing University of Posts and Telecommunications · University of Science and Technology of China
CLIP-Guided SAM: Parameter-Efficient Semantic Conditioning for Promptable Segmentation
May 24, 2026Shayan Jalilian, Abdul BaisVLM Adaptation Text-Guided Image Segmentation
University of Regina, Regina, SK, Canada
Parameter-Efficient VLMs for Gastrointestinal Endoscopy: Medical Image Generation and Clinical Visual Question Answering
May 24, 2026Ojonugwa Oluwafemi Ejiga Peter, Frederick Akor Ejiga, Fahmi Khalifa +1Endoscopy Medical Imaging
Computer Science Department, Morgan State University, Baltimore, USA. · International Organization for Migration (IOM), Geneva, Switzerland. · Electrical & Computer Engineering Department, Morgan State University, Baltimore, USA.
MindAdapter: Few-Shot Parameter-Efficient Residual Calibration of Cross-Subject Brain-to-Visual Decoding Models
May 23, 2026Jiaxiang Liu, Jiawei Du, Xupeng Chen +4Neural Decoding Cross-Modal Alignment
Guangdong Institute of Intelligence Science and Technology Hengqin, China · Agency for Science, Technology and Research Singapore · New York University Brooklyn, US +2
PALoRA: Projection-Adaptive LoRA for Preserving Reasoning in Large Language Models
May 23, 2026Mustafa Hayri Bilgin, Mariam Barry, Albert Bifet +2Knowledge Editing Continual Learning for LLMs
IT Group, Research, BNP Paribas · LTCI, Télécom Paris, Institut Polytechnique de Paris · AI Institute, University of Waikato +1
CrossVLA: Cross-Paradigm Post-Training and Inference Optimization for Vision-Language-Action Models
May 21, 2026Zhi LiuDirect Preference Optimization VLA Model Post-Training
Tianjin University

May 31, 2026J/K move · Enter open · S save

Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning

Anushka Tiwari, Kaiyi Ji

Institute for Artificial Intelligence and Data Science, University at Buffalo, Buffalo, NY, USA · Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA

Storage-Efficient Continual Learning Parameter-Efficient Fine-Tuning Catastrophic Forgetting

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While prompt-based parameter-efficient continual learning mitigates catastrophic forgetting by isolating task-specific prompts, this isolation also limits later tasks from improving earlier ones, leaving backward knowledge transfer underexplored. We address this limitation by proposing Selective bAckward refinement for positive Backward knowledge transfER (SABER), a replay-free framework that enables controlled backward transfer in prompt-based continual learning. SABER determines when backward refinement is beneficial using complementary task-correlation criteria based on prompt-gradient geometry and loss-distribution similarity, and how to perform refinement safely by restricting updates to non-interfering directions in the prompt parameter space. Extensive experiments across multiple continual learning benchmarks and diverse pretrained backbones, including T5-Large, LLaMA, and Qwen, demonstrate that SABER consistently achieves positive backward transfer while maintaining strong overall average performance. Code is available at https://github.com/OptMN-Lab/SABER-ICML-2026/.