Stanford's Real-Time EXPO-FT Makes VLA Robots Real-Time
Stanford researchers including Chelsea Finn released Real-Time EXPO-FT, a reinforcement learning fine-tuning framework that enables slow pretrained VLA policies to run in real time via an asynchronous split that continuously generates candidate action chunks, letting robots keep pace with the physical world.
2026-09-22 ~ 2026-09-24 · 2 related posts
- Real-Time EXPO-FT: async VLA proposals plus RL critics unlock real-time π0.5 — philfung · 2026-09-22
- Stanford's Real-Time EXPO-FT Brings RL to Real-Time VLA Robot Policies, Beats RTC — StanfordAILab · 2026-09-24