The field of generative models is witnessing a significant shift towards the integration of flow matching and particle swarm optimization. Researchers are exploring the intrinsic connections between these two approaches, revealing a duality that can be leveraged to develop novel hybrid algorithms. This understanding has the potential to improve swarm intelligence algorithms and enhance generative models. Notably, flow-based models are being applied to continuous control tasks, demonstrating their ability to capture multimodal action distributions and achieve higher performance than traditional methods. Furthermore, advancements in flow matching are leading to more efficient and effective optimization processes, enabling faster training and improved performance. Noteworthy papers include: Flow Matching Policy Gradients, which introduces a simple on-policy reinforcement learning algorithm that brings flow matching into the policy gradient framework. MixGRPO, which proposes a novel framework that leverages mixed sampling strategies to improve efficiency and boost performance in flow matching models. Weighted Conditional Flow Matching, which modifies the classical CFM loss to produce shorter and straighter trajectories, leading to faster and more accurate generation.