# Sakana AI — SAIL: Scaling In-Context Imitation Learning

- Company: Sakana AI (sakana.ai)
- Announced: 2026-09-27T15:00:00+00:00
- Category: research-paper
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://sakana.ai/sail
- Record: https://forck.live/items/14457-sail-scaling-in-context-imitation-learning
- Subject: Sakana models

Sakana AI and the University of Tokyo propose SAIL, a method for VLM-based robot trajectory generation that uses test-time scaling with Monte Carlo tree search to refine trajectories in simulation. In six simulated manipulation tasks, increasing the search budget from one to 45 candidates raised the success rate from 25% to 73%. The method was also evaluated on a physical robot.

## Evidence

Verbatim from https://sakana.ai/sail:

> Across six manipulation tasks in simulation, increasing the search budget from one candidate to 45 raised the average rate of finding a successful trajectory from 25% to 73%.

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Record: https://forck.live/items/14457-sail-scaling-in-context-imitation-learning
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