The recent advancements in the field of language model optimization and few-shot learning have demonstrated significant progress in efficiency and performance. Researchers are increasingly focusing on techniques that allow for the distillation of knowledge from large language models (LLMs) into smaller, more efficient models, enabling better performance in few-shot scenarios while reducing computational costs. Key innovations include the use of LLMs for data generation and scoring to fine-tune smaller models, advancements in in-context learning distillation, and novel approaches to cross-tokenizer knowledge distillation. These methods not only improve accuracy but also enhance the generalization capabilities of models across different datasets and contexts. Additionally, there is a growing emphasis on knowledge injection techniques that allow LLMs to incorporate new information without extensive retraining, thereby broadening their applicability in dynamic real-world scenarios. Notably, the integration of multi-level optimal transport and prompt distillation methods are particularly promising, offering robust solutions for knowledge transfer and model compression across diverse architectures and parameter sizes.