arXiv AI
Pre-PEFT Probing: Weight Statistics and Perturbation Robustness for Layer Selection in VLM Vision Encoders
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Not All Layers Need Tuning: Diagnosing and Directing Adaptation in Vision-Language-Action Models
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...
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Vision Transformer Finetuning Benefits from Non-Smooth Components
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SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation
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arXiv AI
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Fine-Tuning of Transformer models with Frames
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.
arXiv Machine Learning
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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models
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.