The field of large language models is moving towards developing more personalized and aligned systems. Recent research has focused on enhancing the capabilities of large language models to provide tailored responses to individual users, taking into account their preferences, personality, and attributes. This is achieved through techniques such as reinforcement learning from human feedback, retrieval-augmented generation, and curiosity-driven dialogue systems. Additionally, there is a growing emphasis on aligning language models with human values and preferences, using methods such as structural alignment, pairwise reward modeling, and adversarial training. Notably, papers such as OnRL-RAG and Enhancing Personalized Multi-Turn Dialogue with Curiosity Reward have made significant contributions to the development of personalized dialogue systems, while Align to Structure and A Unified Pairwise Framework for RLHF have advanced the field of language model alignment. Furthermore, papers like Misaligned Roles, Misplaced Images and Adversarial Training of Reward Models have highlighted the importance of robustness and security in language models. Overall, the field is witnessing a shift towards more sophisticated and human-centered language models that can provide effective support and assistance in various applications.