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Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning

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作者您好!这篇文章将GRPO算法应用到了知识图谱+RAG领域非常的令人瞩目,但是如果我们复现的时候手边没有大现存的A100(80G)显卡,比较难以复现代码段中的强化学习部分,同时又很想直接推理结果。请问您是否方便直接提供强化学习完成之后的模型权重?

非常棒的工作! 关于Graph-R1, 我还有如下问题想请教:请问每个数据集的corpus是如何构建的?是基于sample出来的 train&test set的content的构建的还是基于原始数据集的content?另外如果我想使用论文中未用到的数据集来训练,该如何构建corpus呢?

非常棒的工作!!! 我还有几个问题想要请教您: 1.GraphR1论文中的超图构建是否完全基于您上一篇论文的HyperGraphRAG?我计划只对HyperGraphRAG进行一些测试,我看您上一篇论文HyperGraphRAG有更多的细节。我是否可以参考HyperGraphRAG论文,使用本文的超图构建方式进行构建? 2.还有如何对expr下面构建出来的超图文件进行可视化,有无推荐的可视化工具? 期待您的回复! 万分感谢!

作者您好! 这是一份很赞的工作,但是我在部署时遇到了一些问题,具体如下两个bug: 希望能得到您的帮助!

作者您好,我注意到当前的代码实现主要采用了向量检索方式,而未引入图检索机制。考虑到图结构在表达实体关系和上下文方面具有天然优势,图检索在语义推理和结构化信息扩展方面可能更具潜力。请问在设计 Graph-R1 时,出于哪些考虑未采用图检索?是否有相关的性能或复杂度权衡?另外,若希望实现图检索,在这个项目上实现可行吗?

Hi, thank you for sharing your impressive work on Graph-R1! I was impressed by the reported efficiency for constructing the graph. Could you share an estimate of the total cost...

Hi, thanks for releasing the code and paper! Could you share a ballpark training duration so we can better budget the costs that would be necessary to reproduce those results?...

This looks interesting, I have been working with graphRAG for some time now and feel I am getting pretty great results but always looking for more! It looks like this...

From the perspective of the overall workflow, Graph-R1 seems to perform step-by-step decomposition of queries and graph retrieval. What advantages does this offer compared to iterative RAG? (Does it have...

Hi, thank you for sharing this great project! I’m currently studying the codebase and noticed that the script relies on the following JSON files: kv_store_text_chunks.json kv_store_entities.json kv_store_hyperedges.json Could you kindly...