@inproceedings{jiang-etal-2025-rag,
title = "{RAG}-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement",
author = "Jiang, Jinhao and
Chen, Jiayi and
Li, Junyi and
Ren, Ruiyang and
Wang, Shijie and
Zhao, Xin and
Song, Yang and
Zhang, Tao",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.361/",
doi = "10.18653/v1/2025.naacl-long.361",
pages = "7064--7074",
ISBN = "979-8-89176-189-6",
abstract = "Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks. Despite the successes of chain-of-thought and tree-based search methods, they mainly depend on the internal knowledge of LLMs to search over intermediate reasoning steps, limited to dealing with simple tasks involving fewer reasoning steps. In this paper, we propose \textbf{RAG-Star}, a novel RAG approach that integrates the retrieved information to guide the tree-based deliberative reasoning process that relies on the inherent knowledge of LLMs. By leveraging Monte Carlo Tree Search, RAG-Star iteratively plans intermediate sub-queries and answers for reasoning based on the LLM itself. To consolidate internal and external knowledge, we propose a retrieval-augmented verification that utilizes query- and answer-aware reward modeling to provide feedback for the inherent reasoning of LLMs. Our experiments involving Llama-3.1-8B-Instruct and GPT-4o demonstrate that RAG-Star significantly outperforms previous RAG and reasoning methods. Our codes and data are publicly available at https://github.com/RUCAIBox/RAG-Star."
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<abstract>Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks. Despite the successes of chain-of-thought and tree-based search methods, they mainly depend on the internal knowledge of LLMs to search over intermediate reasoning steps, limited to dealing with simple tasks involving fewer reasoning steps. In this paper, we propose RAG-Star, a novel RAG approach that integrates the retrieved information to guide the tree-based deliberative reasoning process that relies on the inherent knowledge of LLMs. By leveraging Monte Carlo Tree Search, RAG-Star iteratively plans intermediate sub-queries and answers for reasoning based on the LLM itself. To consolidate internal and external knowledge, we propose a retrieval-augmented verification that utilizes query- and answer-aware reward modeling to provide feedback for the inherent reasoning of LLMs. Our experiments involving Llama-3.1-8B-Instruct and GPT-4o demonstrate that RAG-Star significantly outperforms previous RAG and reasoning methods. Our codes and data are publicly available at https://github.com/RUCAIBox/RAG-Star.</abstract>
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%0 Conference Proceedings
%T RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
%A Jiang, Jinhao
%A Chen, Jiayi
%A Li, Junyi
%A Ren, Ruiyang
%A Wang, Shijie
%A Zhao, Xin
%A Song, Yang
%A Zhang, Tao
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F jiang-etal-2025-rag
%X Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks. Despite the successes of chain-of-thought and tree-based search methods, they mainly depend on the internal knowledge of LLMs to search over intermediate reasoning steps, limited to dealing with simple tasks involving fewer reasoning steps. In this paper, we propose RAG-Star, a novel RAG approach that integrates the retrieved information to guide the tree-based deliberative reasoning process that relies on the inherent knowledge of LLMs. By leveraging Monte Carlo Tree Search, RAG-Star iteratively plans intermediate sub-queries and answers for reasoning based on the LLM itself. To consolidate internal and external knowledge, we propose a retrieval-augmented verification that utilizes query- and answer-aware reward modeling to provide feedback for the inherent reasoning of LLMs. Our experiments involving Llama-3.1-8B-Instruct and GPT-4o demonstrate that RAG-Star significantly outperforms previous RAG and reasoning methods. Our codes and data are publicly available at https://github.com/RUCAIBox/RAG-Star.
%R 10.18653/v1/2025.naacl-long.361
%U https://aclanthology.org/2025.naacl-long.361/
%U https://doi.org/10.18653/v1/2025.naacl-long.361
%P 7064-7074
Markdown (Informal)
[RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement](https://aclanthology.org/2025.naacl-long.361/) (Jiang et al., NAACL 2025)
ACL
- Jinhao Jiang, Jiayi Chen, Junyi Li, Ruiyang Ren, Shijie Wang, Xin Zhao, Yang Song, and Tao Zhang. 2025. RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7064–7074, Albuquerque, New Mexico. Association for Computational Linguistics.