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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. 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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3. Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

   Kumar, Ananya; Raghunathan, Aditi; Jones, Robbie; Ma, Tengyu; Liang, Percy (January 2022, International Conference on Learning Representations)

   When transferring a pretrained model to a downstream task, two popular methods are full fine-tuning (updating all the model parameters) and linear probing (updating only the last linear layer -- the "head"). It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets (Breeds-Living17, Breeds-Entity30, DomainNet, CIFAR → STL, CIFAR10.1, FMoW, ImageNetV2, ImageNet-R, ImageNet-A, ImageNet-Sketch), fine-tuning obtains on average 2% higher accuracy ID but 7% lower accuracy OOD than linear probing. We show theoretically that this tradeoff between ID and OOD accuracy arises even in a simple setting: fine-tuning overparameterized two-layer linear networks. We prove that the OOD error of fine-tuning is high when we initialize with a fixed or random head -- this is because while fine-tuning learns the head, the lower layers of the neural network change simultaneously and distort the pretrained features. Our analysis suggests that the easy two-step strategy of linear probing then full fine-tuning (LP-FT), sometimes used as a fine-tuning heuristic, combines the benefits of both fine-tuning and linear probing. Empirically, LP-FT outperforms both fine-tuning and linear probing on the above datasets (1% better ID, 10% better OOD than full fine-tuning). 

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4. Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

   Kumar, Ananya; Raghunathan, Aditi; Jones, Robbie; Ma, Tengyu; Liang, Percy (February 2022, ICLR (Oral) 2022 arXiv:2202.10054)

   When transferring a pretrained model to a downstream task, two popular methods are full fine-tuning (updating all the model parameters) and linear probing (updating only the last linear layer—the “head”). It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets (Breeds-Living17, Breeds-Entity30, DomainNet, CIFAR → STL, CIFAR10.1, FMoW, ImageNetV2, ImageNet-R, ImageNet-A, ImageNet-Sketch), fine-tuning obtains on average 2% higher accuracy ID but 7% lower accuracy OOD than linear probing. We show theoretically that this tradeoff between ID and OOD accuracy arises even in a simple setting: fine-tuning overparameterized two-layer linear networks. We prove that the OOD error of fine-tuning is high when we initialize with a fixed or random head—this is because while fine-tuning learns the head, the lower layers of the neural network change simultaneously and distort the pretrained features. Our analysis suggests that the easy two-step strategy of linear probing then full fine-tuning (LP-FT), sometimes used as a fine-tuning heuristic, combines the benefits of both fine-tuning and linear probing. Empirically, LP-FT outperforms both fine-tuning and linear probing on the above datasets (1% better ID, 10% better OOD than full fine-tuning). 

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5. GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

   Zhao, Jiawei; Zhang, Zhenyu; Chen, Beidi; Wang, Zhangyang; Anandkumar, Anima; Tian, Yuandong (July 2024, International Conference on Machine Learning (ICML))

   Training Large Language Models (LLMs) presents significant memory challenges, predominantly due to the growing size of weights and optimizer states. Common memory-reduction approaches, such as low-rank adaptation (LoRA), add a trainable low-rank matrix to the frozen pre-trained weight in each layer, reducing trainable parameters and optimizer states. However, such approaches typically underperform training with full-rank weights in both pre-training and fine-tuning stages since they limit the parameter search to a low-rank subspace and alter the training dynamics, and further, may require full-rank warm start. In this work, we propose Gradient Low-Rank Projection (GaLore), a training strategy that allows full-parameter learning but is more memory-efficient than common low-rank adaptation methods such as LoRA. Our approach reduces memory usage by up to 65.5% in optimizer states while maintaining both efficiency and performance for pre-training on LLaMA 1B and 7B architectures with C4 dataset with up to 19.7B tokens, and on fine-tuning RoBERTa on GLUE tasks. Our 8-bit GaLore further reduces optimizer memory by up to 82.5% and total training memory by 63.3%, compared to a BF16 baseline. Notably, we demonstrate, for the first time, the feasibility of pre-training a 7B model on consumer GPUs with 24GB memory (e.g., NVIDIA RTX 4090) without model parallel, checkpointing, or offloading strategies. 

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