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1. Learning to Prune and Low-Rank Adaptation for Compact Language Model Deployment

   https://doi.org/10.1145/3658617.3697648  

   Ali, Asmer Hamid; Zhang, Fan; Yang, Li; Fan, Deliang (January 2025, ACM)

   Nowadays, parameter-efficient fine-tuning (PEFT) large pre-trained models (LPMs) for downstream task have gained significant popularity, since it could significantly minimize the training computational overhead. The representative work, LoRA [1], learns a low-rank adaptor for a new downstream task, rather than fine-tuning the whole backbone model. However, for inference, the large size of the learned model remains unchanged, leading to in-efficient inference computation. To mitigate this, in this work, we are the first to propose a learning-to-prune methodology specially designed for fine-tuning downstream tasks based on LPMs with low-rank adaptation. Unlike prior low-rank adaptation approaches that only learn the low-rank adaptors for downstream tasks, our method further leverages the Gumbel-Sigmoid tricks to learn a set of trainable binary channel-wise masks that automatically prune the backbone LPMs. Therefore, our method could leverage the benefits of low-rank adaptation to reduce the training parameters size and smaller pruned backbone LPM size for efficient inference computation. Extensive experiments show that the Pruned-RoBbase model with our method achieves an average channel-wise structured pruning ratio of 24.5% across the popular GLUE Benchmark, coupled with an average of 18% inference time speed-up in real NVIDIA A5000 GPU. The Pruned-DistilBERT shows an average of 13% inference time improvement with 17% sparsity. The Pruned-LLaMA-7B model achieves up to 18.2% inference time improvement with 24.5% sparsity, demonstrating the effectiveness of our learnable pruning approach across different models and tasks. 

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2. Prompt-based Adaptation in Large-scale Vision Models: A Survey

   Xi, Xiao; Yunbei, Zhang; Lin, Zhao; Yiyang, Liu; Xiaoying, Liao; Zheda, Mai; Xingjian, Li; Xiao, Wang; Hao, Xu; Jihun, Hamm; et al (January 2026, Transactions on machine learning research)

   Weitong, Zhang (Ed.)

   In computer vision, Visual Prompting (VP) and Visual Prompt Tuning (VPT) have recently emerged as lightweight and effective alternatives to full fine-tuning for adapting large-scale vision models within the “pretrain-then-finetune” paradigm. However, despite rapid progress, their conceptual boundaries remain blurred, as VP and VPT are frequently used interchangeably in current research, reflecting a lack of systematic distinction between these techniques and their respective applications. In this survey, we revisit the designs of VP and VPT from first principles and conceptualize them within a unified framework termed Prompt-based Adaptation (PA). Within this framework, we distinguish methods based on their injection granularity: VP operates at the pixel level, while VPT injects prompts at the token level. We further categorize these methods by their generation mechanism into fixed, learnable, and generated prompts. Beyond the core methodologies, we examine PA’s integrations across diverse domains, including medical imaging, 3D point clouds, and vision language tasks, as well as its role in test-time adaptation and trustworthy AI. We also summarize current benchmarks and identify key challenges and future directions. To the best of our knowledge, we are the first comprehensive survey dedicated to PA’s methodologies and applications in light of their distinct characteristics. Our survey aims to provide a clear roadmap for researchers and practitioners in all areas to understand and explore the evolving landscape of PA-related research. 

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3. DiaBlo: Diagonal Blocks Are Sufficient For Finetuning

   Gurses, Selcuk; Zhang, Aozhong; Deng, Yanxia; Dong, Xun; Li, Xin; Wang, Naigang; Yin, Penghang; Yang, Zi (April 2026, The Fourteenth International Conference on Learning Representations)

   Fine-tuning is a critical step for adapting large language models (LLMs) to domain specific downstream tasks. To mitigate the substantial computational and memory costs of full-model fine-tuning, Parameter-Efficient Fine-Tuning (PEFT) methods have been proposed to update only a small subset of model parameters. However, performance gaps between PEFT approaches and full-model fine-tuning still exist. In this work, we present DiaBlo, a simple yet effective PEFT approach that updates only the diagonal blocks of selected model weight matrices. Unlike Low-Rank Adaptation (LoRA) and its variants, DiaBlo eliminates the need for low-rank matrix products, thereby avoiding the reliance on auxiliary initialization schemes or customized optimization strategies to improve convergence. This design leads to stable and robust convergence while maintaining comparable memory efficiency and training speed to LoRA. Moreover, we provide theoretical guarantees showing that, under mild low-rank conditions, DiaBlo is more expressive than LoRA in the linear problem and converges to a stationary point of the general nonlinear full fine-tuning. Through extensive experiments across a range of tasks—including commonsense reasoning, arithmetic reasoning, code generation, and safety alignment—we show that fine-tuning only diagonal blocks is sufficient for strong and consistent performance. DiaBlo not only achieves competitive accuracy but also preserves high memory efficiency and fast fine-tuning speed. Codes are available at https://github.com/ziyangjoy/DiaBlo. 

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4. WeGeFT: Weight-Generative Fine-Tuning for Multi-Faceted Efficient Adaptation of Large Models

   Savadikar, Chinmay; Song, Xi; Wu, Tianfu (May 2025, Proceedings of the 42 nd International Conference on Machine Learning, Vancouver, Canada. PMLR 267, 2025.)

   Fine-tuning large pretrained Transformer models can focus on either introducing a small number of new learnable parameters (parameter efficiency) or editing representations of a small number of tokens using lightweight modules (representation efficiency). While the pioneering method LoRA (Low-Rank Adaptation) inherently balances parameter, compute, and memory efficiency, many subsequent variants trade off compute and memory efficiency and/or performance to further reduce fine-tuning parameters. To address this limitation and unify parameter-efficient and representation-efficient fine-tuning, we propose Weight-Generative Fine-Tuning (WeGeFT, pronounced wee-gift), a novel approach that learns to generate fine-tuning weights directly from the pretrained weights. WeGeFT employs a simple low-rank formulation consisting of two linear layers, either shared across multiple layers of the pretrained model or individually learned for different layers. This design achieves multifaceted efficiency in parameters, representations, compute, and memory, while maintaining or exceeding the performance of LoRA and its variants. Extensive experiments on commonsense reasoning, arithmetic reasoning, instruction following, code generation, and visual recognition verify the effectiveness of our proposed WeGeFT. 

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5. Antidote: Post-fine-tuning Safety Alignment for Large Language Models against Harmful Fine-tuning Attack

   Huang, Tiansheng; Bhattacharya, Gautam; Joshi, Pratik; Kimball, Josh; Liu, Ling (July 2025, IEEE)

   Safety aligned Large Language Models (LLMs) are vulnerable to harmful fine-tuning attacks (Qi et al., 2023)– a few harmful data mixed in the fine-tuning dataset can break the LLMs’s safety alignment. While several defenses have been proposed, our evaluation shows that existing defenses fail when some specific training hyper-parameters are chosen – a large learning rate or a large number of training epochs in the fine-tuning stage can easily invalidate the defense. To this end, we propose Antidote, a post-fine-tuning stage solution, which remains agnostic to the training hyper-parameters in the fine-tuning stage. Antidote relies on the philosophy that by removing the harmful parameters, the harmful model can be recovered from the harmful behaviors, regardless of how those harmful parameters are formed in the fine-tuning stage. With this philosophy, we introduce a one-shot pruning stage after harmful fine-tuning to remove the harmful weights that are responsible for the generation of harmful content. Despite its embarrassing simplicity, empirical results show that Antidote can reduce harmful score while maintaining accuracy on downstream tasks. 

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