The field of flow-based models is moving towards improving the efficiency and effectiveness of generative models, particularly in applications such as image and video generation, robotic control, and reinforcement learning. Researchers are exploring new methodologies, including conditional optimal transport couplings, ergodic generative flows, and online reinforcement learning, to enhance the performance and speed of these models. Noteworthy papers in this area include:

• Fast Flow-based Visuomotor Policies via Conditional Optimal Transport Couplings, which proposes a methodology for real-time robot action generation.
• Real-Time Person Image Synthesis Using a Flow Matching Model, which achieves near-real-time sampling speeds while maintaining performance comparable to state-of-the-art models.
• Flow-GRPO: Training Flow Matching Models via Online RL, which integrates online reinforcement learning into flow matching models, resulting in significant gains in performance and efficiency.