Sakana AI's SAIL boosts VLM robot trajectory success via test-time scaling

Sakana AI and the University of Tokyo introduced SAIL, a method that generates robot trajectories with a VLM and refines them through simulator testing, VLM feedback, and Monte Carlo tree search. Across six simulated manipulation tasks, raising the search budget from one candidate to 45 increased the success rate of finding a working trajectory from 25% to 73%. The authors also tested the approach on a physical robot, though the post frames further transfer to real hardware as an open question.