arXiv AI September 23, 2026 By Makoto Sato, Yusuke Iwasawa, Yujin Tang, So Kuroki

SAIL: Test-Time Scaling for In-Context Imitation Learning with VLM

SAIL is a framework that transforms robot imitation learning into an iterative refinement problem, enabling test-time scaling of trajectory generation. It employs Monte Carlo Tree Search where each node represents a full trajectory and edges denote refinements, guided by an archive of successful trajectories, a vision‑language model for scoring, and step‑level feedback. Experiments on six manipulation tasks in simulation and real‑world settings show that higher test‑time compute consistently raises success rates, reaching up to 95% on complex tasks.

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