The recent advancements in the field of large language models (LLMs) and vision-language models (VLMs) have been marked by significant strides in safety, robustness, and diversity of model outputs. Researchers are increasingly focusing on mitigating biases, enhancing safety mechanisms, and ensuring that models generate outputs that are representative of diverse populations. A notable trend is the integration of active learning techniques to guide model generation, thereby improving the robustness and representativeness of LLMs in safety-critical scenarios. Additionally, there is a growing emphasis on addressing frequency biases and anisotropy in language model pre-training to ensure better generalization and fairness. The field is also witnessing innovative approaches to fault tolerance in LLM training, with a focus on lightweight yet effective methods to handle computational errors. Furthermore, the sensitivity of generative VLMs to prompt alterations is being thoroughly investigated to enhance the consistency and reliability of model outputs. Overall, the research direction is moving towards creating more inclusive, safe, and reliable AI systems that can handle a wide range of scenarios effectively.