My research aims to make reinforcement learning more efficient by extracting more learning from the experience it collects, and to make interaction with environments cheap and controllable, so that RL can scale to settings where exploration is expensive, including reinforcement learning in the real world. I am currently focusing on computer-use agents and multi-turn reinforcement learning.