The field of language models is moving towards optimizing and improving the efficiency of these models. Recent developments have focused on enhancing the generalization and robustness of language models, as well as reducing the computational costs associated with fine-tuning and adapting these models to specific tasks. Noteworthy papers in this regard include Unified Enhancement of the Generalization and Robustness of Language Models via Bi-Stage Optimization, which proposes a bi-stage optimization framework to improve both generalization and robustness. Another notable paper is LoRASculpt: Sculpting LoRA for Harmonizing General and Specialized Knowledge in Multimodal Large Language Models, which introduces a method to eliminate harmful redundant parameters in Low-Rank Adaptation, thereby improving the performance of multimodal large language models. Additionally, PE-CLIP: A Parameter-Efficient Fine-Tuning of Vision Language Models for Dynamic Facial Expression Recognition achieves competitive performance on benchmark datasets while requiring fewer trainable parameters. These developments highlight the ongoing efforts to improve the efficiency and effectiveness of language models.