---
title: "Reflective RAG — paper by Wu et al. (2026) — summary and ideas"
description: "Agentic RAG systems retrieve over several turns, but they usually cannot judge whether a retrieved passage actually helps. 49 references."
authors: ["Haiyan Wu", "Chenchen Wang", "Chaoqun Sun", "Chengxiong Lu", "Yan-Hong Chen", "Zhiqiang Zhang", "Xiaoqing Feng"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.648/"
doi: "10.18653/v1/2026.findings-acl.648"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wu-2026-reflective-rag-self-evaluation-driven/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wu-2026-reflective-rag-self-evaluation-driven/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/47/) / Reflective RAG: Self-Evaluation Driven Strategy Optimization in Agentic Retrieval-Augmented Generation

# Reflective RAG: Self-Evaluation Driven Strategy Optimization in Agentic Retrieval-Augmented Generation

**Authors:** Haiyan Wu, Chenchen Wang, Chaoqun Sun, Chengxiong Lu, Yan-Hong Chen, [Zhiqiang Zhang](https://md.research.elamur.ai/free/en/authors/zhiqiang-zhang/) and Xiaoqing Feng · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.648/) · [PDF](https://aclanthology.org/2026.findings-acl.648.pdf) · [DOI 10.18653/v1/2026.findings-acl.648](https://doi.org/10.18653/v1/2026.findings-acl.648) · [OpenAlex](https://openalex.org/W7166849228)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Agentic RAG systems retrieve over several turns, but they usually cannot judge whether a retrieved passage actually helps. This can make them commit to an answer too early or reason from misleading evidence. Reflective RAG makes this judgment part of the model's own output. After each retrieval step, the model writes an `<evaluation>` tag that labels the passage Useful, Redundant or Confusing, and that label then shapes its next action.

Training has two stages:
- **SFT stage.** The model is fine-tuned on trajectories it generated itself. Each evaluation label is derived from the perplexity change that retrieval causes, and the trajectory is regenerated wherever the corrected label differs from the original. No external expert models or human annotations are involved.
- **RL stage.** GRPO refines the policy using format and answer rewards. Rollouts are constrained by the evaluation labels, and a KL penalty on the evaluation tokens decays over training.

On five knowledge-intensive QA benchmarks, the method beats strong agentic baselines, trains more stably and generalizes better to multi-hop questions. The work shows that trainable self-critique can replace external judges in retrieval agents.

## Key points
- Each reasoning step follows a fixed tag structure: `<think>`, `<search>`, `<information>`, `<evaluation>`, with `<answer>` at the end. SFT labels come from a λ-percentile threshold over ΔPPL values for each retrieval step.
- Experiments use Qwen2.5-3B-Instruct and Qwen2.5-7B-Instruct, compared against Search-o1 and Search-R1. The datasets include NQ, HotpotQA and 2WikiMHQ.
- Retrieval frequency adapts to model size. The 3B model searches more than the baselines when the evidence looks weak, while the 7B model searches less and stops earlier.
- The authors tested evaluation-label KL coefficients from 0.001 to 0.007, with α₀ = 0.005 under linear decay working best. Values that were too small led to entropy collapse and drift in what the labels meant.
- Limitations:
  - Using the evaluation labels directly as turn-level rewards caused reward hacking, with the model overpredicting "Useful".
  - The three label categories are coarse.
  - Larger models were not tested.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Zhejiang University of Finance and Economics](https://md.research.elamur.ai/free/en/universities/zhejiang-university-of-finance-and-economics/) — University, Hangzhou, China · 2 works

📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,730 works

🧭 Topics (1): [🔍 Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · 552 works

## Cite

Cite as: Haiyan Wu, Chenchen Wang, Chaoqun Sun, Chengxiong Lu, Yan-Hong Chen, Zhiqiang Zhang and Xiaoqing Feng. 2026. Reflective RAG: Self-Evaluation Driven Strategy Optimization in Agentic Retrieval-Augmented Generation. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wu2026reflective,
  title = {{Reflective RAG: Self-Evaluation Driven Strategy Optimization in Agentic Retrieval-Augmented Generation}},
  author = {Haiyan Wu, Chenchen Wang, Chaoqun Sun, Chengxiong Lu, Yan-Hong Chen, Zhiqiang Zhang and Xiaoqing Feng},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.648},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.648}
}
```

- [← previous Reflection in the Dark: Exposing and Escaping the Black Box in Reflective Prompt Optimization](https://md.research.elamur.ai/free/en/papers/liu-2026-reflection-dark-exposing-escaping-black/)
- [next → ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework](https://md.research.elamur.ai/free/en/papers/qin-2026-reflectrm-boosting-generative-reward-models/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/47/)

## References

49 references, 34 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Muhammad Arslan, Hussam Ghanem, Saba Munawar, and Christophe Cruz. 2024. A survey on rag with llms. Procedia computer science, 246:3781–3790. [find](https://scholar.google.com/scholar?q=Muhammad+Arslan%2C+Hussam+Ghanem%2C+Saba+Munawar%2C+and+Christophe+Cruz%2E+2024%2E+A+survey+on+rag+with+llms%2E+Procedia+computer+science%2C+246%3A3781%E2%80%933790%2E)
2. Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024. [Self-rag: Learning to retrieve, generate, and critique through self-reflection](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/). In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024, pages 1–30, Vienna, Austria.…
3. Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013. [Semantic parsing on Freebase from question-answer pairs](https://md.research.elamur.ai/free/en/papers/berant-2013-semantic-parsing-freebase-question-answer/). In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1533–1544, Seattle, Washington, USA. Association for Computational Linguistics.
4. Mingyang Chen, Linzhuang Sun, Tianpeng Li, Haoze Sun, Yijie Zhou, Chenzheng Zhu, Haofen Wang, Jeff Z Pan, Wen Zhang, Huajun Chen, and 1 others. 2025. [Learning to reason with search for llms via reinforcement learning](https://md.research.elamur.ai/free/en/papers/chen-2025-research-learning-reason-search-llms/). arXiv preprint arXiv:2503.19470.
5. Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li. 2024. [A survey on rag meeting llms: Towards retrieval-augmented large language models](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/). In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD ’24, page 6491–650…
6. Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Naik, Pengshan Cai, and Alfio Gliozzo. 2022. [Re2G: Retrieve, rerank, generate](https://md.research.elamur.ai/free/en/papers/glass-2022-re2g-retrieve-rerank-generate/). In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages…
7. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025. [Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/). arXiv preprint arXiv:2501.12948.
8. Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020. [Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps](https://md.research.elamur.ai/free/en/papers/ho-2020-constructing-multi-hop-qa-dataset/). In Proceedings of the 28th International Conference on Computational Linguistics, pages 6609–6625, Barcelona, Spain (Online). International Comm…
9. Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin, Jinfeng Xiao, Zifeng Wang, Jimeng Sun, and Jiawei Han. 2025a. [s3: You don’t need that much data to train a search agent via RL](https://md.research.elamur.ai/free/en/papers/jiang-2025-s3-you-don-t-need/). In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 21599–21617, Suzhou, China. Ass…
10. Yi Jiang, Sendong Zhao, Jianbo Li, Haochun Wang, and Bing Qin. 2025b. GainRAG: Preference alignment in retrieval-augmented generation through gain signal synthesis. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10746–10757,… [find](https://scholar.google.com/scholar?q=Yi+Jiang%2C+Sendong+Zhao%2C+Jianbo+Li%2C+Haochun+Wang%2C+and+Bing+Qin%2E+2025b%2E+GainRAG%3A+Preference+alignment+in+retrieval%2Daugmented+generation+through+gain+signal+synthesis%2E+In+Proceedings+of+the+63rd+Annual+M)
11. Zhengbao Jiang, Frank F Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023. [Active retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/jiang-2023-active-retrieval-augmented-generation/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7969–7992.
12. Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han. 2025. [Search-r1: Training llms to reason and leverage search engines with reinforcement learning](https://md.research.elamur.ai/free/en/papers/jin-2025-search-r1-training-llms-reason/). arXiv preprint arXiv:2503.09516.
13. Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017. [TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/). In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1601–1611, Vancouver…
14. Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023. [Large language models struggle to learn long-tail knowledge](https://md.research.elamur.ai/free/en/papers/kandpal-2022-large-language-models-struggle-learn/). In Proceedings of the 40th International Conference on Machine Learning, ICML’23, Honolulu, Hawaii, USA. JMLR.org.
15. Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. [Dense passage retrieval for open-domain question answering](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769–6781, Onli…
16. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, and 1 others. 2019. [Natural questions: a benchmark for question answering research](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/). Transactions of the Association for Computational Lin…
17. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, and 1 others. 2020. [Retrieval-augmented generation for knowledge-intensive nlp tasks](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/). Advances in neural information processing systems, 33:9459–…
18. Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, and Zhicheng Dou. 2025. [Search-o1: Agentic search-enhanced large reasoning models](https://md.research.elamur.ai/free/en/papers/li-2025-search-o1-agentic-search-enhanced/). arXiv preprint arXiv:2501.05366.
19. Xinze Li, Sen Mei, Zhenghao Liu, Yukun Yan, Shuo Wang, Shi Yu, Zheni Zeng, Hao Chen, Ge Yu, Zhiyuan Liu, and 1 others. 2024. Rag-ddr: Optimizing retrieval-augmented generation using differentiable data rewards. [find](https://scholar.google.com/scholar?q=Xinze+Li%2C+Sen+Mei%2C+Zhenghao+Liu%2C+Yukun+Yan%2C+Shuo+Wang%2C+Shi+Yu%2C+Zheni+Zeng%2C+Hao+Chen%2C+Ge+Yu%2C+Zhiyuan+Liu%2C+and+1+others%2E+2024%2E+Rag%2Dddr%3A+Optimizing+retrieval%2Daugmented+generation+using+differentiable+dat)
20. Jintao Liang, Gang Su, Huifeng Lin, You Wu, Rui Zhao, and Ziyue Li. 2025. Reasoning rag via system 1 or system 2: A survey on reasoning agentic retrieval-augmented generation for industry challenges. arXiv preprint arXiv:2506.10408. [find](https://scholar.google.com/scholar?q=Jintao+Liang%2C+Gang+Su%2C+Huifeng+Lin%2C+You+Wu%2C+Rui+Zhao%2C+and+Ziyue+Li%2E+2025%2E+Reasoning+rag+via+system+1+or+system+2%3A+A+survey+on+reasoning+agentic+retrieval%2Daugmented+generation+for+industry+challenges%2E+)
21. Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101. [find](https://scholar.google.com/scholar?q=Ilya+Loshchilov+and+Frank+Hutter%2E+2017%2E+Decoupled+weight+decay+regularization%2E+arXiv+preprint+arXiv%3A1711%2E05101%2E)
22. Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, and Ajmal Mian. 2025. [A comprehensive overview of large language models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/). ACM Transactions on Intelligent Systems and Technology, 16(5):1–72.
23. Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, and 1 others. 2023. [Check your facts and try again: Improving large language models with external knowledge and automated feedback](https://md.research.elamur.ai/free/en/papers/peng-2023-check-facts-try-again-improving/).
24. Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis. 2023. Measuring and narrowing the compositionality gap in language models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5687–5711. [find](https://scholar.google.com/scholar?q=Ofir+Press%2C+Muru+Zhang%2C+Sewon+Min%2C+Ludwig+Schmidt%2C+Noah+A+Smith%2C+and+Mike+Lewis%2E+2023%2E+Measuring+and+narrowing+the+compositionality+gap+in+language+models%2E+In+Findings+of+the+Association+for+Computati)
25. Qwen, :, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, and 25 others. 2025. Qwen2.5 technical report. Preprint, arXiv:2412.15115. [find](https://scholar.google.com/scholar?q=Qwen%2C+%3A%2C+An+Yang%2C+Baosong+Yang%2C+Beichen+Zhang%2C+Binyuan+Hui%2C+Bo+Zheng%2C+Bowen+Yu%2C+Chengyuan+Li%2C+Dayiheng+Liu%2C+Fei+Huang%2C+Haoran+Wei%2C+Huan+Lin%2C+Jian+Yang%2C+Jianhong+Tu%2C+Jianwei+Zhang%2C+Jianxin+Yang%2C+Jiaxi+)
26. Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023. [In-context retrieval-augmented language models](https://md.research.elamur.ai/free/en/papers/ram-2023-context-retrieval-augmented-language-models/). Transactions of the Association for Computational Linguistics, 11:1316–1331.
27. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, and 1 others. 2024. [Deepseekmath: Pushing the limits of mathematical reasoning in open language models](https://md.research.elamur.ai/free/en/papers/shao-2024-deepseekmath-pushing-limits-mathematical-reasoning/). arXiv preprint arXiv:2402.03300.
28. Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, and Chuan Wu. 2025. [Hybridflow: A flexible and efficient rlhf framework](https://md.research.elamur.ai/free/en/papers/sheng-2024-hybridflow-flexible-efficient-rlhf-framework/). In Proceedings of the Twentieth European Conference on Computer Systems, pages 1279–1297.
29. Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Richard James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2024. [REPLUG: Retrieval-augmented black-box language models](https://md.research.elamur.ai/free/en/papers/shi-2023-replug-retrieval-augmented-black-box/). In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Hum…
30. Aditi Singh, Abul Ehtesham, Saket Kumar, and Tala Talaei Khoei. 2025. Agentic retrieval-augmented generation: A survey on agentic rag. arXiv preprint arXiv:2501.09136. — [arXiv:2501.09136](https://arxiv.org/abs/2501.09136) [find](https://scholar.google.com/scholar?q=Aditi+Singh%2C+Abul+Ehtesham%2C+Saket+Kumar%2C+and+Tala+Talaei+Khoei%2E+2025%2E+Agentic+retrieval%2Daugmented+generation%3A+A+survey+on+agentic+rag%2E+arXiv+preprint+arXiv%3A2501%2E09136%2E+%E2%80%94+%5BarXiv%3A2501%2E09136%5D%28https%3A%2F%2Farx)
31. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022. [Text embeddings by weakly-supervised contrastive pre-training](https://md.research.elamur.ai/free/en/papers/wang-2022-text-embeddings-weakly-supervised-contrastive/).
32. Suyuchen Wang, Jinlin Wang, Xinyu Wang, Shiqi Li, Xiangru Tang, Sirui Hong, Xiao-Wen Chang, Chenglin Wu, and Bang Liu. 2025. Improving context fidelity via native retrieval-augmented reasoning. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 21216–212… [find](https://scholar.google.com/scholar?q=Suyuchen+Wang%2C+Jinlin+Wang%2C+Xinyu+Wang%2C+Shiqi+Li%2C+Xiangru+Tang%2C+Sirui+Hong%2C+Xiao%2DWen+Chang%2C+Chenglin+Wu%2C+and+Bang+Liu%2E+2025%2E+Improving+context+fidelity+via+native+retrieval%2Daugmented+reasoning%2E+In+Pro)
33. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, and 1 others. 2022. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). Advances in neural information processing systems, 35:24824– 24837.
34. Quan Wei, Siliang Zeng, Chenliang Li, William Brown, Oana Frunza, Wei Deng, Anderson Schneider, Yuriy Nevmyvaka, Yang Katie Zhao, Alfredo Garcia, and Mingyi Hong. 2025. Reinforcing multi-turn reasoning in llm agents via turn-level reward design. Preprint, arXiv:2505.11821. — [arXiv:2505.11821](https://arxiv.org/abs/2505.11821) [find](https://scholar.google.com/scholar?q=Quan+Wei%2C+Siliang+Zeng%2C+Chenliang+Li%2C+William+Brown%2C+Oana+Frunza%2C+Wei+Deng%2C+Anderson+Schneider%2C+Yuriy+Nevmyvaka%2C+Yang+Katie+Zhao%2C+Alfredo+Garcia%2C+and+Mingyi+Hong%2E+2025%2E+Reinforcing+multi%2Dturn+reasonin)
35. Zhepei Wei, Wei-Lin Chen, and Yu Meng. 2024. Instructrag: Instructing retrieval-augmented generation via self-synthesized rationales. [find](https://scholar.google.com/scholar?q=Zhepei+Wei%2C+Wei%2DLin+Chen%2C+and+Yu+Meng%2E+2024%2E+Instructrag%3A+Instructing+retrieval%2Daugmented+generation+via+self%2Dsynthesized+rationales%2E)
36. Shicheng Xu, Liang Pang, Huawei Shen, Xueqi Cheng, and Tat-Seng Chua. 2024. Search-in-the-chain: Interactively enhancing large language models with search for knowledge-intensive tasks. In Proceedings of the ACM Web Conference 2024, pages 1362–1373. [find](https://scholar.google.com/scholar?q=Shicheng+Xu%2C+Liang+Pang%2C+Huawei+Shen%2C+Xueqi+Cheng%2C+and+Tat%2DSeng+Chua%2E+2024%2E+Search%2Din%2Dthe%2Dchain%3A+Interactively+enhancing+large+language+models+with+search+for+knowledge%2Dintensive+tasks%2E+In+Proceedings)
37. Yongxin Xu, Ruizhe Zhang, Xinke Jiang, Yujie Feng, Yuzhen Xiao, Xinyu Ma, Runchuan Zhu, Xu Chu, Junfeng Zhao, and Yasha Wang. 2025. [Parenting: Optimizing knowledge selection of retrieval-augmented language models with parameter decoupling and tailored tuning](https://md.research.elamur.ai/free/en/papers/xu-2025-parenting-optimizing-knowledge-selection-retrieval/). In Proceedings of the 63rd Annual Meetin…
38. Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling. 2024. [Corrective retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/yan-2024-corrective-retrieval-augmented-generation/).
39. Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen, Zhipeng Chen, Wayne Xin Zhao, Lei Fang, and Ji-Rong Wen. 2025. [R1-searcher: Incentivizing the search capability in llms via reinforcement learning](https://md.research.elamur.ai/free/en/papers/song-2025-r1-searcher-incentivizing-search/). arXiv preprint arXiv:2503.05592.
40. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. [HotpotQA: A dataset for diverse, explainable multi-hop question answering](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/). In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2369…
41. Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022. Musique: Multi-hop questions via single-hop question composition. Transactions of the Association for Computational Linguistics, 10:539–554. [find](https://scholar.google.com/scholar?q=Harsh+Trivedi%2C+Niranjan+Balasubramanian%2C+Tushar+Khot%2C+and+Ashish+Sabharwal%2E+2022%2E+Musique%3A+Multi%2Dhop+questions+via+single%2Dhop+question+composition%2E+Transactions+of+the+Association+for+Computational+Li)
42. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. 2022. [React: Synergizing reasoning and acting in language models](https://md.research.elamur.ai/free/en/papers/yao-2022-react-synergizing-reasoning-acting-language/). In The eleventh international conference on learning representations.
43. Guibin Zhang, Hejia Geng, Xiaohang Yu, Zhenfei Yin, Zaibin Zhang, Zelin Tan, Heng Zhou, Zhongzhi Li, Xiangyuan Xue, Yijiang Li, and 1 others. 2025a. The landscape of agentic reinforcement learning for llms: A survey. arXiv preprint arXiv:2509.02547. — [arXiv:2509.02547](https://arxiv.org/abs/2509.02547) [find](https://scholar.google.com/scholar?q=Guibin+Zhang%2C+Hejia+Geng%2C+Xiaohang+Yu%2C+Zhenfei+Yin%2C+Zaibin+Zhang%2C+Zelin+Tan%2C+Heng+Zhou%2C+Zhongzhi+Li%2C+Xiangyuan+Xue%2C+Yijiang+Li%2C+and+1+others%2E+2025a%2E+The+landscape+of+agentic+reinforcement+learning+for)
44. Kaiyan Zhang, Yuxin Zuo, Bingxiang He, Youbang Sun, Runze Liu, Che Jiang, Yuchen Fan, Kai Tian, Guoli Jia, Pengfei Li, and 1 others. 2025b. A survey of reinforcement learning for large reasoning models. arXiv preprint arXiv:2509.08827. — [arXiv:2509.08827](https://arxiv.org/abs/2509.08827) [find](https://scholar.google.com/scholar?q=Kaiyan+Zhang%2C+Yuxin+Zuo%2C+Bingxiang+He%2C+Youbang+Sun%2C+Runze+Liu%2C+Che+Jiang%2C+Yuchen+Fan%2C+Kai+Tian%2C+Guoli+Jia%2C+Pengfei+Li%2C+and+1+others%2E+2025b%2E+A+survey+of+reinforcement+learning+for+large+reasoning+model)
45. Tianjun Zhang, Shishir G Patil, Naman Jain, Sheng Shen, Matei Zaharia, Ion Stoica, and Joseph E Gonzalez. 2024. [Raft: Adapting language model to domain specific rag](https://md.research.elamur.ai/free/en/papers/zhang-2024-raft-adapting-language-model-domain/).
46. Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, and 1 others. 2023. [Siren’s song in the ai ocean: a survey on hallucination in large language models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/).
47. Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, and 1 others. 2023. [A survey of large language models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/). arXiv preprint arXiv:2303.18223, 1(2).
48. Xuhui Zheng, Kang An, Ziliang Wang, Yuhang Wang, and Yichao Wu. 2025. [StepSearch: Igniting LLMs search ability via step-wise proximal policy optimization](https://md.research.elamur.ai/free/en/papers/zheng-2025-stepsearch-igniting-llms-search-ability/). In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 21816–21841, Suzhou, China. Association for Comp…
49. Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, and Zheyan Luo. 2024. [LlamaFactory: Unified efficient fine-tuning of 100+ language models](https://md.research.elamur.ai/free/en/papers/zheng-2024-llamafactory-unified-efficient-fine-tuning/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 400–410, Bangkok, Thail…

*Updated 2 November 2026*