@inproceedings{guo-etal-2026-routerag,
title = "{R}oute{RAG}: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning",
author = "Guo, Yucan and
Su, Miao and
Guan, Saiping and
Sun, Zihao and
Jin, Xiaolong and
Guo, Jiafeng and
Cheng, Xueqi",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1502/",
pages = "30042--30059",
ISBN = "979-8-89176-395-1",
abstract = "Retrieval-Augmented Generation (RAG) integrates non-parametric knowledge into Large Language Models (LLMs), typically from unstructured texts and structured graphs. While recent progress has advanced text-based RAG to multi-turn reasoning through Reinforcement Learning (RL), extending these advances to hybrid retrieval introduces additional challenges. Existing graph-based or hybrid systems typically depend on fixed or handcrafted retrieval pipelines, lacking the ability to integrate supplementary evidence as reasoning unfolds. Besides, while graph evidence provides relational structures crucial for multi-hop reasoning, it is substantially more expensive to retrieve. To address these limitations, we introduce RouteRAG, an RL-based framework that enables LLMs to perform multi-turn and adaptive graph-text hybrid RAG. RouteRAG jointly optimizes the entire generation process via RL, allowing the model to learn when to reason, what to retrieve from either texts or graphs, and when to produce final answers, all within a unified generation policy. To guide this learning process, we design a two-stage training framework that accounts for both task outcome and retrieval efficiency, enabling the model to exploit hybrid evidence while avoiding unnecessary retrieval overhead. Experimental results across five question answering benchmarks demonstrate that RouteRAG significantly outperforms existing RAG baselines, highlighting the benefits of end-to-end RL in supporting adaptive and efficient retrieval for complex reasoning."
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<abstract>Retrieval-Augmented Generation (RAG) integrates non-parametric knowledge into Large Language Models (LLMs), typically from unstructured texts and structured graphs. While recent progress has advanced text-based RAG to multi-turn reasoning through Reinforcement Learning (RL), extending these advances to hybrid retrieval introduces additional challenges. Existing graph-based or hybrid systems typically depend on fixed or handcrafted retrieval pipelines, lacking the ability to integrate supplementary evidence as reasoning unfolds. Besides, while graph evidence provides relational structures crucial for multi-hop reasoning, it is substantially more expensive to retrieve. To address these limitations, we introduce RouteRAG, an RL-based framework that enables LLMs to perform multi-turn and adaptive graph-text hybrid RAG. RouteRAG jointly optimizes the entire generation process via RL, allowing the model to learn when to reason, what to retrieve from either texts or graphs, and when to produce final answers, all within a unified generation policy. To guide this learning process, we design a two-stage training framework that accounts for both task outcome and retrieval efficiency, enabling the model to exploit hybrid evidence while avoiding unnecessary retrieval overhead. Experimental results across five question answering benchmarks demonstrate that RouteRAG significantly outperforms existing RAG baselines, highlighting the benefits of end-to-end RL in supporting adaptive and efficient retrieval for complex reasoning.</abstract>
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%0 Conference Proceedings
%T RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning
%A Guo, Yucan
%A Su, Miao
%A Guan, Saiping
%A Sun, Zihao
%A Jin, Xiaolong
%A Guo, Jiafeng
%A Cheng, Xueqi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F guo-etal-2026-routerag
%X Retrieval-Augmented Generation (RAG) integrates non-parametric knowledge into Large Language Models (LLMs), typically from unstructured texts and structured graphs. While recent progress has advanced text-based RAG to multi-turn reasoning through Reinforcement Learning (RL), extending these advances to hybrid retrieval introduces additional challenges. Existing graph-based or hybrid systems typically depend on fixed or handcrafted retrieval pipelines, lacking the ability to integrate supplementary evidence as reasoning unfolds. Besides, while graph evidence provides relational structures crucial for multi-hop reasoning, it is substantially more expensive to retrieve. To address these limitations, we introduce RouteRAG, an RL-based framework that enables LLMs to perform multi-turn and adaptive graph-text hybrid RAG. RouteRAG jointly optimizes the entire generation process via RL, allowing the model to learn when to reason, what to retrieve from either texts or graphs, and when to produce final answers, all within a unified generation policy. To guide this learning process, we design a two-stage training framework that accounts for both task outcome and retrieval efficiency, enabling the model to exploit hybrid evidence while avoiding unnecessary retrieval overhead. Experimental results across five question answering benchmarks demonstrate that RouteRAG significantly outperforms existing RAG baselines, highlighting the benefits of end-to-end RL in supporting adaptive and efficient retrieval for complex reasoning.
%U https://aclanthology.org/2026.findings-acl.1502/
%P 30042-30059
Markdown (Informal)
[RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning](https://aclanthology.org/2026.findings-acl.1502/) (Guo et al., Findings 2026)
ACL
- Yucan Guo, Miao Su, Saiping Guan, Zihao Sun, Xiaolong Jin, Jiafeng Guo, and Xueqi Cheng. 2026. RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 30042–30059, San Diego, California, United States. Association for Computational Linguistics.