The field is currently witnessing significant developments in diffusion models and multi-objective optimization. Researchers are focusing on improving the efficiency and stability of diffusion models, particularly in offline reinforcement learning settings. Novel methods, such as variational adaptive weighting and frequency-decoupled guidance, are being proposed to enhance the performance of these models. Meanwhile, advancements in multi-objective optimization are leading to the development of new algorithms with improved convergence rates and sample complexity. Noteworthy papers include: Fast and Stable Diffusion Planning through Variational Adaptive Weighting, which achieves competitive performance with up to 10 times fewer training steps. Guidance in the Frequency Domain Enables High-Fidelity Sampling at Low CFG Scales, which proposes frequency-decoupled guidance to improve image quality at low guidance scales. STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning, which introduces a new algorithm with state-of-the-art convergence rates and sample complexities.