The field of large language models is moving towards more efficient and effective fine-tuning methods, with a focus on preserving general capabilities while adapting to specific tasks. Recent studies have shown that the choice of fine-tuning tasks and acquisition functions can significantly impact knowledge retention and model performance. Additionally, research on the loss landscape of large language models has revealed that pre-training and fine-tuning create distinct basins, which can inform the development of more robust fine-tuning methods. Noteworthy papers include Data Doping or True Intelligence?, which highlights the importance of task selection in updating LLM knowledge, and LoKI: Low-damage Knowledge Implanting of Large Language Models, which proposes a PEFT technique that preserves general capabilities while achieving task-specific performance. Bayesian Optimization for Enhanced Language Models also presents a novel approach to optimizing acquisition functions for fine-tuning, resulting in improved generalization and performance.