The field of deep learning is moving towards more efficient and effective optimization techniques, with a focus on generalization and robustness. Recent developments have led to the creation of new algorithms and methods that improve upon existing ones, such as mixed-mode differentiation, sharpness-aware minimization, and adaptive gradient algorithms. These advancements have shown promising results in reducing computational costs, improving convergence rates, and enhancing model performance. Notably, some papers have introduced innovative approaches to existing problems, such as the use of Z-score gradient filtering and the development of novel loss functions. Furthermore, research has also explored the intersection of optimization and generalization, with studies on the effect of importance weighting and the development of methods to mitigate catastrophic overfitting. Overall, the field is witnessing a significant shift towards more principled and efficient optimization methods, with a strong emphasis on generalization and real-world applicability. Noteworthy papers include: * Scalable Meta-Learning via Mixed-Mode Differentiation, which proposes a practical algorithm for efficient meta-learning. * Focal-SAM, which introduces a novel sharpness-aware minimization technique for long-tailed classification. * Learning from Loss Landscape, which presents a generalizable mixed-precision quantization approach via adaptive sharpness-aware gradient aligning.