SAIL: Scientific Agentic Intelligence via a Science-Aware Loop

Key points

• SAIL is developed through a science-aware improvement loop: agents built on frontier AI models analyze its task failures and construct training tasks that address the underlying capability gaps.1
• The agents draw on paper collections and scientific code repositories to build problems, interaction trajectories, and executable tasks with the required environments and tools.1
• We repeat this loop over multiple development cycles and train SAIL through supervised fine-tuning, specialist training, multi-teacher on-policy distillation, and agentic reinforcement learning.1
• SAIL achieves competitive performance across scientific research tasks with substantially fewer parameters than leading open-weight models.1

Sources (1)

• [1]SAIL: Scientific Agentic Intelligence via a Science-Aware Loop
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 08:04 AM

  We introduce SAIL, an open model with 35B total and 3B active parameters for literature research, scientific coding, and multi-step research workflows.

  SAIL is developed through a science-aware improvement loop: agents built on frontier AI models analyze its task failures and construct training tasks that address the underlying capability gaps.

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