The field of natural language processing and vision-language understanding is rapidly evolving, with a focus on improving the efficiency and effectiveness of language model training and vision-language models. Recent developments have highlighted the importance of high-quality pretraining data, innovative methods for fine-tuning and adapting models to specialized domains, and the need for robust and generalizable models that can handle complex scenes and negation. Noteworthy papers in this area include RePro, which introduces a novel web recycling method for pretraining language models, and Learning Dynamics of VLM Finetuning, which proposes a two-stage recipe for optimizing vision-language models. Other notable works include What Not to Detect, which addresses the limitation of affirmative bias in vision-language models, and CoT-PL, which employs structured visual chain-of-thought reasoning for open-vocabulary object detection. These advancements have the potential to significantly improve the performance and applicability of language models and vision-language models in real-world applications.