Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However, its effectiveness is hindered by a fundamental disconnect: the knowledge graph (KG) construction process is decoupled from its downstream application, yielding suboptimal graph structures. To bridge this gap, we introduce AutoGraph-R1, the first framework to directly optimize KG construction for task performance using Reinforcement Learning (RL). AutoGraph-R1 trains an LLM constructor by framing graph generation as a policy learning problem, where the reward is derived from the graph's functional utility in a RAG pipeline. We design two novel, task-aware reward functions, one for graphs as knowledge carriers and another as knowledge indices. Across multiple QA benchmarks, AutoGraph-R1 consistently enables graph RAG methods to achieve significant performance gains over using task-agnostic baseline graphs. Our work shows it is possible to close the loop between construction and application, shifting the paradigm from building intrinsically ``good'' graphs to building demonstrably ``useful'' ones.
@article{arxiv.2510.15339,
title = {AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction},
author = {Hong Ting Tsang and Jiaxin Bai and Haoyu Huang and Qiao Xiao and Tianshi Zheng and Baixuan Xu and Shujie Liu and Yangqiu Song},
journal= {arXiv preprint arXiv:2510.15339},
year = {2026}
}