---
title: "ReSearch — paper by Chen et al. (2025) — summary and ideas"
description: "Multi-hop questions force an LLM to look up information several times and decide at each step what… 42 references, cited by 88 papers here."
authors: ["Mingyang Chen", "Linzhuang Sun", "Tianpeng Li", "Haoze Sun", "Yijie Zhou", "Chenzheng Zhu", "Haofen Wang", "Jeff Z. Pan", "Wen Zhang", "Huajun Chen", "Fan Yang", "Zenan Zhou", "Weipeng Chen"]
year: 2025
venue: "arXiv:2503.19470"
source: "https://arxiv.org/abs/2503.19470"
doi: "10.48550/arxiv.2503.19470"
arxiv: "2503.19470"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chen-2025-research-learning-reason-search-llms/"
---
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# ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning

**Authors:** [Mingyang Chen](https://md.research.elamur.ai/free/en/authors/mingyang-chen/), [Linzhuang Sun](https://md.research.elamur.ai/free/en/authors/linzhuang-sun/), [Tianpeng Li](https://md.research.elamur.ai/free/en/authors/tianpeng-li/), [Haoze Sun](https://md.research.elamur.ai/free/en/authors/haoze-sun/), [Yijie Zhou](https://md.research.elamur.ai/free/en/authors/yijie-zhou/), [Chenzheng Zhu](https://md.research.elamur.ai/free/en/authors/chenzheng-zhu/), [Haofen Wang](https://md.research.elamur.ai/free/en/authors/haofen-wang/), [Jeff Z. Pan](https://md.research.elamur.ai/free/en/authors/jeff-z-pan/), [Wen Zhang](https://md.research.elamur.ai/free/en/authors/wen-zhang/), [Huajun Chen](https://md.research.elamur.ai/free/en/authors/huajun-chen/), [Fan Yang](https://md.research.elamur.ai/free/en/authors/fan-yang/), [Zenan Zhou](https://md.research.elamur.ai/free/en/authors/zenan-zhou/) and [Weipeng Chen](https://md.research.elamur.ai/free/en/authors/weipeng-chen/) · **Year:** 2025 · **Venue:** [arXiv:2503.19470](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/) ([NeurIPS 2025](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2025/)) · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2503.19470) · [PDF](https://arxiv.org/pdf/2503.19470) · [DOI 10.48550/arxiv.2503.19470](https://doi.org/10.48550/arxiv.2503.19470) · [arXiv 2503.19470](https://arxiv.org/abs/2503.19470) · [OpenAlex](https://openalex.org/W4416256501)

**Citations:** 11 citations (OpenAlex, 2026-10-14) · [88](https://md.research.elamur.ai/free/en/papers/chen-2025-research-learning-reason-search-llms/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Multi-hop questions force an LLM to look up information several times and decide at each step what to query next. Most multi-step RAG pipelines rely on hand-crafted prompts or heuristics, and annotating intermediate retrieval-and-reasoning traces is costly. ReSearch lets the model learn this interleaving on its own through reinforcement learning. Each rollout mixes free-text thinking, search queries written inside special tags, and retrieved passages that the environment inserts back into the context. Generation pauses at a closing search tag, fetches results and then resumes. Training uses GRPO with a simple rule-based reward on the outcome. Retrieved tokens are masked out of the loss, so only text the model wrote is optimized. No annotated reasoning chains are needed. Qwen2.5 models at 7B and 32B scale, both base and Instruct, were trained on a single dataset. They outperform prompting-based baselines on four multi-hop QA benchmarks, and reflection and self-correction emerge without being designed in. The work shows that outcome-only RL can cheaply teach search-augmented reasoning.

## Key points
- Rollout format uses `<think>`, `<search>`, `<result>` and `<answer>` tags. Two minimal prompt templates are provided: one serves as direct input for base models, the other as a system prompt for Instruct models.
- Optimization uses GRPO, which takes its baseline from group-normalized rewards instead of a critic and adds a KL penalty. Retrieval-result tokens are excluded from the loss.
- Evaluation covers HotpotQA, 2Wiki, MuSiQue and Bamboogle, scored with LLM-as-a-Judge among other measures. Baselines are Naive Generation, Naive RAG, IRCoT and Iter-RetGen, all built on Qwen2.5-32B-Instruct.
- Reported absolute gains over baselines range from 8.9% to 22.4%, despite training on just one training set.
- The paper appeared at NeurIPS 2025 and comes from Baichuan Inc. with academic partners. Code is available at github.com/Agent-RL/ReSearch.

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

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## Cite

Cite as: Mingyang Chen, Linzhuang Sun, Tianpeng Li, Haoze Sun, Yijie Zhou, Chenzheng Zhu, Haofen Wang, Jeff Z. Pan, Wen Zhang, Huajun Chen, Fan Yang, Zenan Zhou and Weipeng Chen. 2025. ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning. arXiv:2503.19470.

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

```bibtex
@misc{chen2025research,
  title = {{ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning}},
  author = {Mingyang Chen, Linzhuang Sun, Tianpeng Li, Haoze Sun, Yijie Zhou, Chenzheng Zhu, Haofen Wang, Jeff Z. Pan, Wen Zhang, Huajun Chen, Fan Yang, Zenan Zhou and Weipeng Chen},
  year = {2025},
  doi = {10.48550/arxiv.2503.19470},
  eprint = {2503.19470},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2503.19470}
}
```

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40. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. [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 EMNLP, pages 2369–2380. Association for Computational Linguistics, 2018.
41. Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah D. Goodman. [Star: Bootstrapping reasoning with reasoning](https://md.research.elamur.ai/free/en/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/). In NeurIPS, 2022.
42. Wayne Xin Zhao, Jing Liu, Ruiyang Ren, and Ji-Rong Wen. Dense text retrieval based on pretrained language models: A survey. ACM Trans. Inf. Syst., 42(4):89:1–89:60, 2024. [find](https://scholar.google.com/scholar?q=Wayne+Xin+Zhao%2C+Jing+Liu%2C+Ruiyang+Ren%2C+and+Ji%2DRong+Wen%2E+Dense+text+retrieval+based+on+pretrained+language+models%3A+A+survey%2E+ACM+Trans%2E+Inf%2E+Syst%2E%2C+42%284%29%3A89%3A1%E2%80%9389%3A60%2C+2024%2E)

*Updated 2 November 2026*