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arXiv · 2603.08269

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

Makoto Sato·Yusuke Iwasawa·Yujin Tang·So Kuroki
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Abstract

In-context imitation learning allows robots to acquire skills from demonstrations, yet one-shot trajectory generation remains fragile under environmental variation. We propose SAIL, a framework that reframes robot imitation as an iterative refinement problem capable of scaling with test-time compute. SAIL utilizes Monte Carlo Tree Search, where each node is a complete trajectory and edges correspond to trajectory refinements. The process is guided by three core components: an automated archive of successful trajectories for contextually relevant retrieval, a vision language model-based scoring mechanism for trajectory evaluation, and a step-level feedback that provides trajectory-aligned scores for iterative refinement. Experiments across six diverse manipulation tasks in simulation and real-world validation clearly demonstrate that increasing test-time compute consistently improves success rates, achieving up to 95% on complex tasks. Our results suggest that trajectory-level test-time scaling is a robust path toward more generalizable robotic agents.

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Makoto Sato, Yusuke Iwasawa, Yujin Tang, So Kuroki. 2026-03-09. SAIL: Test-Time Scaling for In-Context Imitation Learning with VLM. https://arxiv.org/abs/2603.08269

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