Abstract
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 \textit{GraphWalker}, a novel agentic KGQA framework that addresses these challenges through \textit{Automated Trajectory Synthesis} and \textit{Stage-wise Fine-tuning}. GraphWalker adopts a two-stage SFT training paradigm: First, the agent is trained on structurally diverse trajectories synthesized from constrained random-walk paths, establishing a broad exploration prior over the KG; Second, the agent is further fine-tuned on a small set of expert trajectories to develop reflection and error recovery capabilities. Extensive experiments demonstrate that our stage-wise SFT paradigm unlocks a higher performance ceiling for a lightweight reinforcement learning (RL) stage, enabling GraphWalker to achieve state-of-the-art performance on CWQ and WebQSP. Additional results on GrailQA and our constructed GraphWalkerBench confirm that GraphWalker enhances generalization to out-of-distribution reasoning paths. The code is publicly available at this https URL
Abstract (translated)
智能体知识图谱问答（KGQA）要求智能体与知识图谱（KG）进行迭代交互，面临训练数据稀缺和推理泛化能力不足的双重挑战。具体而言，现有方法常限制智能体的探索能力：基于提示的方法缺乏自主导航训练，而当前训练流程通常将推理局限于预定义轨迹。为此，本文提出一种新型智能体KGQA框架GraphWalker，通过自动化轨迹合成与分阶段微调解决上述挑战。GraphWalker采用两阶段监督微调（SFT）范式：首先，智能体在从受限随机游走路径合成的结构多样化轨迹上进行训练，建立对KG的广泛探索先验；其次，智能体在小规模专家轨迹上进一步微调，发展反思与纠错能力。大量实验表明，我们提出的分阶段SFT范式为轻量级强化学习（RL）阶段释放了更高性能上限，使GraphWalker在CWQ和WebQSP数据集上达到最先进性能。在GrailQA及我们构建的GraphWalkerBench上的额外结果证实，GraphWalker增强了对分布外推理路径的泛化能力。代码已公开于此https URL。
URL
https://arxiv.org/abs/2603.28533
