Sergey Levine's team proposes ARLI to guide large robot foundation models with small RL policies

Sergey Levine shared research conducted with Siemens and Berkeley on how to pair a small RL policy with a large robot foundation model under real-time chunking (RTC). Because the RL policy infers faster, it can observe newer images, thereby steering the foundation model's policy toward better behavior. The introduction he cited notes that asynchronous VLA inference reduces inference latency but breaks the Markov assumption required for reinforcement learning fine-tuning, and ARLI is proposed to address this problem. The project page is https://async-rl-intermediate-information.github.io/ . The work was led by Brian Zhu, Momen Khalil, Emanuele Poggi (Siemens), and @ehharrison4 (Berkeley), among others.