<ul data-eligibleForWebStory="true"><li>Generative models like diffusion and flow-matching provide expressive policies for offline reinforcement learning.</li><li>A new approach called Single-Step Completion Policy (SSCP) is introduced to enhance generative policy training by predicting direct completion vectors, enabling accurate one-shot action generation.</li><li>SSCP combines the richness of generative models with the efficiency of unimodal policies, offering improved training and inference speed without the need for long backpropagation chains.</li><li>SSCP not only performs well in standard offline RL and behavior cloning benchmarks but also supports goal-conditioned RL, making it a versatile and efficient framework for deep RL and sequential decision-making.</li></ul>

Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning

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