arXiv:2605.23365v1 Announce Type: new Abstract: Diffusion and flow matching have emerged as expressive policy classes in reinforcement learning, but their reliance on multi-step denoising imposes substantial computational overhead at inference time, which is particularly problematic in online RL. MeanFlow offers a promising alternative by learning an average velocity field that maps noise to data in a single network evaluation. However, MeanFlow typically requires samples from the target distribution to construct its target velocity field, which are unavailable in online RL. We propose Score-B
This research addresses a critical computational bottleneck in applying advanced policy classes like diffusion models to online reinforcement learning, which is a rapidly evolving field.
Improving the efficiency of policy optimization in online RL can accelerate the development and deployment of more capable and adaptive AI agents in real-world scenarios.
The proposed Score-Based One-step MeanFlow Policy Optimization could enable faster training and inference for sophisticated AI models, particularly in dynamic environments where rapid decision-making is crucial.
- · AI/ML researchers
- · Robotics developers
- · SaaS platforms employing AI agents
- · Industries adopting online RL for automation
- · Traditional multi-step RL methods
- · Systems with high inference latency tolerance
More efficient and capable online reinforcement learning systems become feasible, reducing the computational cost of deploying complex AI.
This efficiency gain could accelerate the development of autonomous AI agents across various industries, making them more practical for real-time applications.
Widespread adoption of such efficient RL could lead to new types of automated services and products currently bottlenecked by computational demands of learning.
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Read at arXiv cs.LG