The field of neural network fine-tuning is witnessing significant advancements, with a focus on improving efficiency and adaptability. Researchers are exploring novel methods to reduce computational costs and storage requirements, while maintaining or enhancing performance. One notable direction is the development of parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA) and its variants, which enable efficient model adaptation while reducing computational overhead. Another area of interest is the development of methods that can adapt to new tasks and domains without requiring extensive retraining or fine-tuning. These advancements have the potential to make neural networks more accessible and applicable to a wide range of tasks and domains. Noteworthy papers include: Gradient-Informed Fine-Tuning (GIFT) which achieves up to 28% relative accuracy improvement compared to the baseline performance under noise misspecification. WaRA, a novel PEFT method that leverages wavelet transforms to decompose the weight update matrix into a multi-resolution representation, performs superior on diverse vision tasks. GORP, a novel training strategy that synergistically combines full and low-rank parameters, overcomes the limitations of LoRA and achieves superior performance on continual learning benchmarks.