arXiv:2606.11087v1 Announce Type: new Abstract: Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simulated and real robot control. While they are known to scale stably in the supervised imitation learning setting, incorporating them into reinforcement learning (RL) pipelines for policy improvement has proven more difficult. It often requires specialized training objectives or backpropagating through denoising processes, which cause well-known issues with stability and affect scalability. In this pa
This paper addresses a known technical challenge in integrating advanced continuous control policies (like diffusion/flow models) into reinforcement learning pipelines, which is critical for pushing the boundaries of AI capabilities in control tasks.
Improving the stability and scalability of reinforcement learning with expressive policies directly accelerates the development of more capable and autonomous AI systems, particularly in robotics and complex control scenarios.
The ability to stably incorporate powerful generative models into RL offers a path to more effective policy improvement, potentially leading to faster and more robust learning for advanced AI agents.
- · AI researchers
- · Robotics companies
- · Automation sector
More robust and efficient training of AI agents for complex physical and digital tasks.
Accelerated development of general-purpose AI systems capable of learning and adapting in dynamic environments.
Enhanced automation across industries, potentially impacting labor markets and operational efficiencies on a larger scale.
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