The field of AI alignment and optimization is rapidly evolving, with a focus on developing innovative methods to align large language models with human preferences and values. Recent research has explored the use of robust fine-tuning algorithms, probabilistic modeling of latent agentic substructures, and deep reinforcement learning to improve the alignment of AI systems. These advances have the potential to significantly improve the performance and safety of AI systems in a variety of applications, including public health and multimodal interaction. Notable papers in this area include: Optimizing Health Coverage in Ethiopia, which proposes a learning-augmented approach to optimize health coverage in Ethiopia. Preference Robustness for DPO with Applications to Public Health, which introduces a robust fine-tuning algorithm for designing reward functions in public health applications. Murphys Laws of AI Alignment, which reframes alignment debates around structural limits and trade-offs. Icon2, which leverages inherent regulation of LLMs' representation space for efficient and tailored preference dataset construction. Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks, which develops a theory of intelligent agency grounded in probabilistic modeling for neural models. Aligning Large Vision-Language Models by Deep Reinforcement Learning and Direct Preference Optimization, which explores paradigms for fine-tuning LVLMs using DRL and DPO techniques.