Fine-Tuning

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Overview

Fine-tuning starts from a pretrained model and uses task-specific data or feedback to adapt its predictions, representations, or behavior. Approaches range from updating all weights to parameter-efficient methods such as LoRA and adapters, selective layer or neuron updates, and supervised, preference-based, or reinforcement-learning objectives. The techniques are used across language, vision, speech, robotics, and scientific applications, with evaluation spanning task accuracy, domain transfer, safety, and deployment costs. A central challenge is gaining new capabilities without losing useful prior knowledge: outcomes depend on the model, data, and training choices, and improvements on one benchmark may not transfer to other settings.

Latest papers

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250 papers23 papers in the last four weeks, down 30% on the four weeks before. 0.1% of all new papers.

  1. Rethinking Fine-Tuning: Unlocking Hidden Capabilities in Vision-Language Models

    Dec 28, 2025Mingyuan Zhang, Yue Bai, Yifan Wang +2Fine-TuningVLM Adaptation

    College of Engineering, Northeastern University · Khoury College of Computer Science, Northeastern University

  2. When does Subgroup Balance Matter? Predicting Performance Sensitivity from Latent Separability

    Dec 10, 2025Anissa Alloula, Charles Jones, Zuzanna Wakefield-Skorniewska +2Subgroup RobustnessDataset Bias

    University of Oxford · Imperial College London

  3. MarkTune: Improving the Quality-Detectability Trade-off in Model-Embedded LLM Watermarking

    Dec 3, 2025Yizhou Zhao, Zhiwei Steven Wu, Adam BlockLLM WatermarkingRobust Watermarking

    University of Pennsylvania · Carnegie Mellon University · Columbia University

  4. FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning

    Nov 22, 2025Bo Yin, Xiaobin Hu, Xingyu Zhou +7Fine-TuningAdaptive Model Routing

    National University of Singapore · University of Electronic Science and Technology of China · Nanyang Technological University +4

  5. Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

    Sep 29, 2025Xin Qiu, Yulu Gan, Conor F. Hayes +6Fine-TuningEvolutionary Optimization

    Cognizant AI Lab, San Francisco, CA, USA · Massachusetts Institute of Technology, Cambridge, MA, USA · University of California, Los Angeles, Los Angeles, CA, USA +1

  6. When is Fine-Tuning Scalable? Certified Recovery from Noisy Data and Linear-Cost Fine-Tuning on Structured Task Shifts

    Aug 31, 2025Anastasis Kratsios, Tin Sum Cheng, Bum Jun Kim +1Fine-TuningOOD Generalization

    Department of Mathematics, McMaster University, Canada · Department of Mathematics and Computer Science, University of Basel, Switzerland · Department of Statistical Sciences, University of Toronto, Canada +1

  7. When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts

    Jul 19, 2025Wooseok Ha, Yuansi ChenFine-TuningDomain Adaptation

    Department of Mathematical Sciences, KAIST · Department of Mathematics, ETH Zürich

  8. A Closer Look at Dynamic Scene Graph Generation In the Era of Multimodal Large Language Models

    Mar 20, 2025Xuanming Cui, Jaiminkumar Ashokbhai Bhoi, Chionh Wei Peng +2Scene Graph GenerationVideo SGG

    University of Central Florida · DSO National Laboratories

  9. GenTL: A General Transfer Learning Model for Building Thermal Dynamics

    Jan 23, 2025Fabian Raisch, Thomas Krug, Christoph Goebel +1Fine-TuningTransfer Learning

    Technical University of Applied Sciences Rosenheim Rosenheim, Germany · Technical University of Munich Munich, Germany

  10. Optimizing Three Critical Factors for Practical and Effective OOD Detection Fine-Tuning

    Aug 2, 2023Hyunjun Choi, JaeHo Chung, Hawook JeongFine-TuningOOD Detection

    ASRI, ECE., Seoul National University, South Korea · RideFlux Inc., South Korea