AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction
Summary: arXiv:2510.15339v3
Announce Type: cross
Abstract
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 a large language model (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.
Key Features of AutoGraph-R1
- Direct Optimization: The framework directly links KG construction with task performance, ensuring that the graphs built are aligned with specific applications.
- Reinforcement Learning Approach: Utilizing a policy learning framework allows the model to learn from the utility of the generated graphs, adapting and optimizing in real-time.
- Task-Aware Reward Functions: The introduction of task-specific rewards enhances the model’s ability to evaluate the efficacy of the constructed graphs.
Performance Improvements
Across multiple QA benchmarks, AutoGraph-R1 consistently enables graph RAG methods to achieve significant performance gains over using task-agnostic baseline graphs. This advancement highlights the importance of aligning the construction of knowledge graphs with the specific requirements of downstream applications, ultimately leading to better performance in question answering tasks.
Conclusion
Our work demonstrates that it is possible to close the loop between construction and application, shifting the paradigm from building intrinsically “good” graphs to building demonstrably “useful” ones. As the field of artificial intelligence continues to evolve, frameworks like AutoGraph-R1 represent a significant step forward in the development of knowledge graphs that not only serve as repositories of information but also enhance the efficiency and effectiveness of question answering systems.