Adapter Tuning

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Adapter tuning studies how to specialize pretrained language, vision, speech, and multimodal models by training small added modules while leaving most or all of the backbone frozen. Common approaches include bottleneck adapters, low-rank updates such as LoRA, prompts, and input- or feature-space modules; researchers also vary where adapters are placed, how their rank is allocated, and whether experts are routed by task or input. These methods are evaluated on settings such as language understanding and reasoning, few-shot vision, speech recognition, retrieval, and continual learning, with attention to trainable parameters, accuracy, inference cost, and forgetting. The central tension is how to give adapters enough capacity and flexibility to match full fine-tuning while keeping them efficient, composable, and reliable across tasks and domains.

Latest papers

76 papers3 papers in the last four weeks, down 57% on the four weeks before. 0.0% of all new papers.

Manifold-Constrained Residual Adaptation with Frame-Adaptive Layer Aggregation for Speaker Verification
Oct 19, 2026Zezhong Jin, Xiaoyu Wang, Zhe Li +4Speaker Verification Adapter Tuning
Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong SAR · Baidu Inc. · Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
Gradient Routing Localizes and Removes Unintended Behaviors in RL
Oct 12, 2026Jake Ward, Shawn Hu, Aria Wong +4Reward Hacking RL Post-Training
MATS · Stanford · FAR AI +1
FUSEye: Training-Light Fisheye Detection with Overlapping Views and Zero-Initialized Adapters
Oct 2, 2026Wenya Su, Kai Luo, Di Wen +5Object Detection Efficient Object Detection
School of Artificial Intelligence and Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, China · Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Germany
EHRAdapt: Adapting Pretrained Language Models to Electronic Health Records with Semantic Priors for Rare Clinical Events
Sep 27, 2026Andre R Goncalves, Vincent Liu, Priyadip RayDomain Adaptation for LLMs Long-Tail Learning
Lawrence Livermore National Laboratory · Kaiser Permanente
TaskAnchor: Grounding Task State in Reactive VLAs for Long-Horizon Manipulation
Sep 20, 2026Hengyan Liu, Wenlve Zhou, Bo Yue +7Memory-Augmented VLMs Long-Horizon Robotic Manipulation
The Chinese University of Hong Kong, Shenzhen · Foshan University · DexForce +1
VT-Bridge: Bridging Pretrained Foundation VLAs to VTLAs via Lightweight Residual Adaptation
Sep 18, 2026Yansong Wu, Tuo Yang, Rongping Zhao +5Tactile-Vision-Language-Action Models Contact-Rich Robotic Manipulation
Technical University of Munich, Germany · Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Shanghai University, China
Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLMs
Sep 16, 2026Mohan Shi, Zilai Wang, Natarajan Balaji Shankar +3Domain Adaptation for ASR Adapter Tuning
Department of Electrical and Computer Engineering University of California Los Angeles Los Angeles, USA
Multi-modal Knowledge Preserving Adapter for Embedding Backward Compatibility
Sep 15, 2026Jaeseok Byun, Gukyeong Kwon, Han-Kai Hsu +4Multimodal Large Language Models Adapter Tuning
1Seoul National University, South Korea · 2Amazon AGI, USA
CAL-MOS: Bridging Layers with Adapters for Robust MOS Prediction Across Speech Foundation Models
Sep 14, 2026Alef Iury Siqueira Ferreira, Pedro Lustosa Rege Botelho, Fernanda Silva +5Speech Foundation Models Speech Quality Assessment
AKCIT, Brazil · Federal University of Rio Grande do Norte (UFRN), Brazil · Federal University of Technology (UTFPR), Brazil +1
KanAdapter: A Kolmogorov-Arnold Network-based Plug-and-Play Module for Efficient Fine-tuning of Foundation Speech Models
Sep 4, 2026Phuong Tuan Dat, Phuong Khai Minh, Tran Huy DatAdapter Tuning Parameter-Efficient Fine-Tuning
Dept. of Electrical and Computer Engineering, National University of Singapore · School of Computing, National University of Singapore · Institute of Advanced Intelligence and Computing (IAIC), A∗STAR
FCPRAG: Fusion-Controller Parametric Retrieval-Augmented Generation for Stable Multi-Passage LoRA Injection
Aug 22, 2026Jinchang Zhu, Jindong Li, Yi Ding +5Retrieval-Augmented Generation RAG Robustness
The Hong Kong University of Science and Technology (Guangzhou) · University of Macau · Institute of Artificial Intelligence (TeleAI), China Telecom
SAPE: Sandwich Adapters for Parameter Efficiency in Large Language Model Fine-Tuning
Aug 15, 2026Mohammad Aref Jafari-Raddani, Morteza Mohajjel KafshdoozParameter Sharing Parameter-Efficient Fine-Tuning
Department of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran.
TACTICL: Task-Aware Compression of Tabular ICL Models
Aug 11, 2026Mykhailo Koshil, Matthias Feurer, Katharina EggenspergerModel Compression Tabular Foundation Models
TU Dortmund University, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Germany
Modality-Gated Deep Adapters: Adding a Modality to a Frozen Embedding Model with Exact Preservation
Aug 10, 2026Abdul Basit Tonmoy, Kazi Fardinul Hoque, Md. Shahrier Islam Arham +1Multimodal Embedding Adapter Tuning
Eximius Labs · Wabash College · Skop Intelligence Co.
Beyond Rotations: AuroOFT for Expressive Quantized Orthogonal Fine-Tuning
Aug 5, 2026Yue Han, Dianlin WangLLM Quantization Fine-Tuning
College of Systems Engineering, National University of Defense Technology · College of Computer Science and Technology, National University of Defense Technology
Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning
Aug 5, 2026Yue Han, Ziniu LiuSupervised Fine-Tuning Multi-Task Learning
College of Systems Engineering, National University of Defense Technology · College of Computer Science and Technology, National University of Defense Technology
Parameter-Efficient Adapter Tuning for Tabular-Image Multimodal Learning
Jun 10, 2026Jiaqi LuoFine-Tuning Adapter Tuning
School of Mathematical Sciences, Soochow University, No.1 Shizi Street, Suzhou, 215006, Jiangsu Province, China
Spatial-Temporal Decoupled Adapter for Micro-gesture Online Recognition
Jun 5, 2026Xucheng Shen, Kun Li, Fei Wang +3Micro-Gesture Recognition Fine-Tuning
Hefei University of Technology, Hefei, China · United Arab Emirates University, Al Ain, United Arab Emirates · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China +1
TALAN: Task-Aligned Latent Adaptation Networks for Targeted Post-Training of Large Language Models
Jun 5, 2026Chengkai Zhang, Ziteng Liu, Junpu Wang +4Supervised Fine-Tuning Adapter Tuning
Meta AI
RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit
Jun 4, 2026Amirhossein Ghaffari, Ali Goodarzi, Huong Nguyen +3LLM Evaluation LLM Fine-Tuning
Future Computing Group University of Oulu Oulu, Finland · Centre for Applied Computing University of Oulu Oulu, Finland
GLASS: GRPO-Trained LoRA for Acoustic Style Steering in Zero-Shot Text-to-Speech
Jun 4, 2026Jaehoon Kang, Yejin Lee, Kyuhong ShimTTS Synthesis Adapter Tuning
Department of Artificial Intelligence, Sungkyunkwan University, Korea
TaDA: Calibrated Probe Gating for Task-Domain LoRA Merging
Jun 3, 2026Huy Quoc To, Fuyi Li, Guangyan Huang +1Adapter Tuning Low-Rank Adaptation
Deakin University, Melbourne, VIC, Australia · Adelaide University, Adelaide, SA, Australia
Towards Pretraining Text Encoders for TabPFN
Jun 3, 2026Mustafa Tajjar, Alexander Pfefferle, Lennart Purucker +1Tabular Foundation Models Fine-Tuning
Department of Computer Science, University of Freiburg, Freiburg, Germany · ELLIS Institute Tübingen, Tübingen, Germany · Prior Labs, Freiburg, Germany.
Compress then Merge: From Multiple LoRAs into One Low-Rank Adapter
Jun 2, 2026Zhengbao He, Ruiqi Ding, Zhehao Huang +3Adapter Tuning Low-Rank Adaptation
Institute of Image Processing and Pattern Recognition, School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China · Shanghai Key Laboratory of Flexible Medical Robotics, Tongren Hospital, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
ProtoAda: Prototype-Guided Adaptive Adapter Expansion and Geometric Consolidation for Multimodal Continual Instruction Tuning
Jun 1, 2026Yu-Cheng Shi, Zhen-Hao Xie, Jun-Tao Tang +1Large Vision-Language Models Adapter Tuning
School of Artificial Intelligence, Nanjing University, China · State Key Laboratory of Novel Software Technology, Nanjing University, China
MASER: Modality-Adaptive Specialist Routing for Embodied 3D Spatial Intelligence
Jun 1, 2026Hilton Raj, Vishnuram AVEmbodied QA Adapter Tuning
Boston University
On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters
Jun 1, 2026Mind Lab, :, Vin Bo +64LLM Fine-Tuning Adapter Tuning
Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks
Jun 1, 2026Nermeen Abou Baker, David Rohrschneider, Uwe HandmannFine-Tuning Instance Segmentation
Ruhr West University of applied sciences · Lützowstraße 5, 46236 Bottrop, Germany: Ruhr West University of Applied Sciences
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

Oct 19, 2026J/K move · Enter open · S save

Manifold-Constrained Residual Adaptation with Frame-Adaptive Layer Aggregation for Speaker Verification

Zezhong Jin, Xiaoyu Wang, Zhe Li, Chong-Xin Gan+3

Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong SAR · Baidu Inc. · Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA

Speaker Verification Adapter Tuning

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Large self-supervised speech models provide powerful representations for speaker verification, but full fine-tuning is expensive. Most conventional adapters insert small trainable modules to modify the output of each layer, while keeping the original residual connections unchanged. Building on our preliminary study of manifold-constrained Hyper-Connections in speaker encoders, we explore parameter-efficient adaptation of both residual paths and feature selection across layers. We first propose a Manifold-Constrained Residual Adapter (MCRA), which splits the hidden dimensions of each Transformer residual state into N groups and uses a small Sinkhorn-normalized matrix to exchange information among them. A learnable gate controls the balance between the original and mixed residual paths. The gate adds only one scalar parameter at each residual connection and initially favors the original path, allowing gradual adaptation. We further propose Frame-Adaptive Layer Aggregation (FALA), an attention-based aggregation method that uses a single learned query shared across all utterances to compute layer weights separately for each speech frame. Because the layer representations depend on the input, the resulting weights vary across both utterances and frames, unlike the fixed weights of a conventional layer-weighted sum. Compared with a frozen WavLM baseline using a fixed layer-weighted sum, our method achieves relative EER reductions of 22.4% on VoxCeleb1-O and 2.9% on CN-Celeb, with trainable adaptation parameters equivalent to only 0.0009% and 0.0016% of the WavLM-Large parameters, respectively.