arXiv:2603.10263v2 Announce Type: replace-cross Abstract: We introduce Distribution Contractive Reinforcement Learning (DICE-RL), a framework that uses reinforcement learning (RL) as a "distribution contraction" operator to refine pretrained generative robot policies. DICE-RL turns a pretrained behavior prior into a high-performing "pro" policy by amplifying high-success behaviors from online feedback. We pretrain a diffusion- or flow-based policy for broad behavioral coverage, then finetune it with a stable, sample-efficient residual off-policy RL framework that combines selective behavior re
The AI/robotics research community is actively seeking more efficient and stable methods to train complex robotic policies, leveraging advanced RL techniques for faster skill acquisition.
This development proposes a method to significantly accelerate the transition of 'prior' robotic behaviors into high-performing 'pro' capabilities, reducing barriers to deploying sophisticated robot skills.
Robot policy training can become more sample-efficient and stable, allowing for quicker deployment of generative policies into real-world, high-performance applications.
- · Robotics companies
- · AI researchers
- · Automation sector
- · Traditional RL methods requiring extensive data
Further acceleration in the development and deployment of advanced robotic capabilities.
Increased feasibility of 'general purpose' robots as skill acquisition becomes less resource-intensive.
Potential for an inflection point in humanoid robotics and complex automated systems as training bottlenecks diminish.
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Read at arXiv cs.LG