The field of large language models (LLMs) is moving towards more efficient and robust adaptation methods. Recent developments focus on reducing the effects of overfitting and degeneralization, especially in low-data settings. Techniques such as corrective self-distillation, prompt-conditioned parameter generation, and sparse fine-tuning are being explored to improve the performance of LLMs on specific tasks while maintaining their general-purpose capabilities. Notably, some studies have demonstrated the importance of mitigating forgetting during domain-specific continued pre-training and the benefits of using novel training recipes to strengthen performance on both translation and general-purpose tasks. Noteworthy papers include: Minifinetuning, which introduces a method for language model domain adaptation that reduces the effects of overfitting-induced degeneralization. EvoLM, which presents a model suite that enables systematic and transparent analysis of LMs' training dynamics across multiple stages. Drag-and-Drop LLMs, which proposes a prompt-conditioned parameter generator that eliminates per-task training by mapping a handful of unlabeled task prompts directly to LoRA weight updates.