SAKE: Structured Agentic Knowledge Extrapolation for Complex LLM Reasoning via Reinforcement Learning

Understand

Knowledge extrapolation is the process of inferring novel information by combining and extending existing knowledge that is explicitly available.

• It is essential for solving complex questions in specialized domains where retrieving comprehensive external knowledge is impractical.
• We propose SAKE (Structured Agentic Knowledge Extrapolation), a RL powered agentic framework that trains LLMs to autonomously retrieve and extrapolate structured knowledge through tool-augmented reinforcement learning.
• SAKE defines two external KG tools: entity group construction and cross-group triplet retrieval.