The field of large language models (LLMs) is moving towards more efficient and effective fine-tuning methods. Recent developments have focused on improving the adaptation of LLMs to specialized tasks, particularly in resource-constrained environments. Notable advancements include the use of low-rank adaptation techniques, template-oriented reasoning, and hierarchical fine-tuning strategies. These innovations have led to significant improvements in model performance, efficiency, and stability. Furthermore, research has also explored the importance of incorporating nonlinearity in fine-tuning methods, as well as the development of latent thought-augmented training frameworks. Overall, the field is shifting towards more sophisticated and efficient fine-tuning techniques that can unlock the full potential of LLMs. Noteworthy papers include: Sensitivity-LoRA, which proposes a dynamic rank allocation method for low-rank adaptation, and NoRA, which introduces a framework for adapting nonlinear activation functions in pretrained transformer-based models. HEFT is also notable for its hierarchical fine-tuning strategy that combines low-rank adaptation and representation fine-tuning.