One Step Is Enough: Dispersive MeanFlow Policy Optimization

• URL: http://arxiv.org/abs/2601.20701v1
• Date: Wed, 28 Jan 2026 15:34:29 GMT
• Title: One Step Is Enough: Dispersive MeanFlow Policy Optimization
• Abstract summary: Dispersive MeanFlow Policy Optimization (DMPO) is a unified framework that enables one-step generation.<n> Experiments across RoboMimic manipulation and OpenAI Gym locomotion benchmarks demonstrate competitive or superior performance.
• Score: 23.382067451764396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time robotic control demands fast action generation. However, existing generative policies based on diffusion and flow matching require multi-step sampling, fundamentally limiting deployment in time-critical scenarios. We propose Dispersive MeanFlow Policy Optimization (DMPO), a unified framework that enables true one-step generation through three key components: MeanFlow for mathematically-derived single-step inference without knowledge distillation, dispersive regularization to prevent representation collapse, and reinforcement learning (RL) fine-tuning to surpass expert demonstrations. Experiments across RoboMimic manipulation and OpenAI Gym locomotion benchmarks demonstrate competitive or superior performance compared to multi-step baselines. With our lightweight model architecture and the three key algorithmic components working in synergy, DMPO exceeds real-time control requirements (>120Hz) with 5-20x inference speedup, reaching hundreds of Hertz on high-performance GPUs. Physical deployment on a Franka-Emika-Panda robot validates real-world applicability.

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