EXPO-FT uses reinforcement learning post-training to bring π0.5 to a 30/30 success rate on all 8 manipulation tasks

Through reinforcement learning post-training, EXPO-FT brought π0.5 to a 30/30 success rate on all 8 manipulation tasks tested, using an average of only 19 minutes of reinforcement learning data per task. The method targets challenging manipulation tasks, including routing string lights and inserting a power connector to light the string, potting a billiard ball, and placing a flower into a wine bottle. It is released as an efficient, reliable, open-source VLA fine-tuning method; the paper and video are at https://pd-perry.github.io/expo-ft/.