GraphWalker: Agentic Knowledge Graph Question Answering via Synthetic Trajectory Curriculum
Agentic knowledge graph question answering (KGQA) requires an agent to iteratively interact with knowledge graphs (KGs), posing challenges in both training data scarcity and reasoning generalization. Specifically, existing approaches often restrict agent exploration: prompting-based methods lack autonomous navigation training, while current training pipelines usually confine reasoning to predefined trajectories. To this end, this paper proposes GraphWalker, a novel agentic KGQA framework that addresses these challenges through Automated Trajectory Synthesis and Stage-wise Fine-tuning. GraphWalker adopts a two-stage SFT training paradigm: First, the agent is trained on structurally diverse...
| Authors | Shuwen Xu, Yao Xu, Jiaxiang Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 March 2026 |
| Last updated at source | 7 August 2026 |
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