Recent advancements in large language models (LLMs) have showcased a variety of new capabilities and approaches aimed at improving interaction with and understanding of human language. Among these, instruction tuning has emerged as a method to enhance pre-trained LLMs by fine-tuning them with pairs of instructions and desired outcomes, enabling these models to perform real-world tasks more effectively. Additionally, the development of AnyGPT, a unified multimodal LLM, represents a significant step forward. AnyGPT utilizes discrete sequence modeling for processing various modalities including speech, text, images, and music, allowing for a more versatile application across different media. Another notable innovation is the introduction of LongAgent, which scales LLMs to handle 128k context through multi-agent collaboration, addressing the challenge of long context windows. Furthermore, CoLLaVO, a large language and vision model, signifies progress in combining language models with visual data, while Google's Learning to Learn Faster from Human Feedback with Language Model Predictive Control demonstrates how LLMs can be trained to improve their performance based on human feedback, including writing robot code from language commands. These developments highlight the ongoing evolution and potential of LLMs in various applications.