Refined Policy Distillation (RPD) transforms general-purpose Vision-Language-Action Models into highly specialized robotic policies with enhanced performance.

• Addresses key limitation of VLA models: good generalization but suboptimal task success rates
• Combines reinforcement learning with knowledge distillation to create task-specific expert policies
• Achieves 22% higher success rates than base models on manipulation tasks
• Requires 90% less training data than conventional RL approaches

This research bridges the gap between versatile but underperforming generalist models and specialized expert systems, enabling more efficient deployment of advanced robotics in industrial settings.

Refined Policy Distillation: From VLA Generalists to RL Experts