The paper introduces Mask Fine‑Tuning (MFT), a new approach for adapting Vision‑Language Models that avoids modifying backbone weights. MFT learns masks to selectively route information through existing pretrained connections, dynamically uncovering subnetworks that better align with downstream tasks. Experiments demonstrate that MFT consistently outperforms both Full Fine‑Tuning and Parameter‑Efficient Fine‑Tuning across multiple benchmarks, while also offering insights into how pretrained VLMs reorganize their internal pathways during adaptation.
By Mingyuan Zhang, Yue Bai, Yifan Wang, Yiyang Huang, Yun Fu
arXiv:2609.18084v1 Announce Type: cross
Abstract: Fine-tuning a Vision-Language-Action (VLA) model for a new deployment environment is expensive, yet most methods apply uniform-capacity adapters to e...
By Shahram Najam Syed, Arthur Jakobsson, Prayuj Sachdev, Jeffrey Ichnowski
arXiv:2602. 06883v3 Announce Type: replace Abstract: The smoothness of the transformer architecture has been extensively studied in the context of generalization, training stability, and adversarial robustness.
By Ambroise Odonnat, Laetitia Chapel, Romain Tavenard, Ievgen Redko
arXiv:2606. 03879v1 Announce Type: cross Abstract: As foundation models scale toward fusing more heterogeneous visual streams, understanding how diverse encoders interact under joint training becomes a prerequisite for principled design.
By Wei Ding, Yudong Zhang, Ruobing Xie, Xingwu Sun, Jiansheng Chen, Yu Wang
SIMPLER is a pre‑fine‑tuning method that reduces inference and deployment costs for Earth Observation foundation models by pruning redundant layers. It uses layer‑wise representation similarity on unlabeled task data to identify and remove up to 79% of parameters without requiring gradients, magnitude heuristics, or hyperparameter tuning. Experiments on Prithvi‑EO‑2, TerraMind, and ImageNet‑pretrained ViT‑MAE show that SIMPLER retains 94% of baseline performance while achieving 2.1× faster training and 2.6× faster inference.
By V\'ictor Barreiro, Johannes Jakubik, Francisco Arg\"uello, Dora B. Heras
arXiv:2610.03389v1 Announce Type: cross
Abstract: Vision language models (VLMs) incur substantial inference cost because every visual token is processed by the attention and MLP projections of every...
By Rahul Chowdhury, Timothy A Rupprecht, Xuan Shen, Shaoyi Huang, Pu Zhao, Yanzhi Wang
The paper presents a retraining‑free technique that transforms pre‑trained transformers into elastic models capable of adapting to any computational budget on the fly. By jointly ranking every MLP neuron and attention head on a common scale and applying soft pruning across multiple sparsity targets, the method achieves smooth performance degradation up to 60% sparsity while outperforming one‑shot baselines. Experiments on 27 vision and text transformer encoders show significant parameter reductions with minimal accuracy loss and notable throughput and latency gains on GPUs, all produced in minutes.
By Walter Simoncini, Dheeraj Varghese, Gertjan J. Burghouts, Cees G. M. Snoek
The paper introduces FrameFT, a parameter-efficient fine-tuning method for transformer models that represents weight updates using sparse coefficients in a Fusion Frame basis. This approach reduces memory usage by storing only the sparse coefficients, leading to significant compute advantages and formal convergence guarantees. Experiments on language and vision tasks show that FrameFT matches or surpasses state‑of‑the‑art PEFT techniques while requiring far fewer trainable parameters.
By Harshavardhan Adepu, Li Zhang, Sanjiv Kumar, Vikas Singh
arXiv:2609.37759v1 Announce Type: cross
Abstract: Vision-language models (VLMs) exhibit strong multimodal capabilities but remain vulnerable to backdoors implanted through poisoned fine-tuning data....
By Shuming Liu, Zhifang Zhang, Suqin Yuan, Khin Mi Mi Aung, Zhuoyi Lin, Lei Feng
arXiv:2608. 06901v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved remarkable generalization across diverse multimodal tasks through large-scale pre-training, yet their rapidly increasing computational and memory requirements pose significant challenges for deployment in constrained environments.
By Minseok Kang, Hyunwoo Kim, Chanyoung Kim, Minwoo Kim, Jaekoo Lee, Dahuin Jung
arXiv:2610.27698v1 Announce Type: cross
Abstract: Vision-language-action (VLA) models transfer knowledge from large-scale pretraining into robot control, but their manipulation performance remains se...
By Borui Xu, Han Chen, Bingsheng He, Ming Wu, Jason Zeng, Michael Heinrich
arXiv:2609.25655v1 Announce Type: new
Abstract: As large language models (LLMs) scale rapidly, dense full-parameter adaptation becomes increasingly expensive, motivating sparse and modular architectu...
By Zhentao Tan, Chang Liu, Yao Liu, Yue Wu, Jieping Ye