code/RL_model/verl/Search-R1/verl.egg-info/PKG-INFO

Metadata-Version: 2.4
Name: verl
Version: 0.1
Summary: veRL: Volcano Engine Reinforcement Learning for LLM
Home-page: https://github.com/volcengine/verl
Author: Bytedance - Seed - MLSys
Author-email: Bytedance - Seed - MLSys <zhangchi.usc1992@bytedance.com>, Bytedance - Seed - MLSys <gmsheng@connect.hku.hk>
License: 
                                         Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright [yyyy] [name of copyright owner]
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
        
Project-URL: Homepage, https://github.com/volcengine/verl
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: accelerate
Requires-Dist: codetiming
Requires-Dist: datasets
Requires-Dist: dill
Requires-Dist: hydra-core
Requires-Dist: numpy
Requires-Dist: pybind11
Requires-Dist: ray
Requires-Dist: tensordict
Requires-Dist: transformers<4.48
Requires-Dist: vllm<=0.6.3
Provides-Extra: test
Requires-Dist: pytest; extra == "test"
Requires-Dist: yapf; extra == "test"
Dynamic: author
Dynamic: home-page
Dynamic: license-file

# Search-R1: Train your LLMs to reason and call a search engine with reinforcement learning

<div align="center">
  <img src="https://raw.githubusercontent.com/PeterGriffinJin/Search-R1/main/public/logo.png" alt="logo" width="300"/>
</div>

<p align="center">
  <a href="https://arxiv.org/abs/2503.09516">
    <img src="https://img.shields.io/badge/Paper1-blue?style=for-the-badge" alt="Button1"/>
  </a>
  <a href="https://arxiv.org/abs/2505.15117">
    <img src="https://img.shields.io/badge/Paper2-green?style=for-the-badge" alt="Button2"/>
  </a>
  <a href="https://huggingface.co/collections/PeterJinGo/search-r1-67d1a021202731cb065740f5">
    <img src="https://img.shields.io/badge/Resources-orange?style=for-the-badge" alt="Button3"/>
  </a>
  <a href="https://x.com/BowenJin13/status/1895544294473109889">
    <img src="https://img.shields.io/badge/Tweet-red?style=for-the-badge" alt="Button4"/>
  </a>
  <a href="https://wandb.ai/peterjin/Search-R1-v0.2">
    <img src="https://img.shields.io/badge/Logs-purple?style=for-the-badge" alt="Button5"/>
  </a>
</p>


<!-- <strong>Search-R1</strong> is a reinforcement learning framework for <em>training reasoning and searching (tool-call) interleaved LLMs</em>.  -->
<!-- We built upon [veRL](https://github.com/volcengine/verl). -->
**Search-R1** is a reinforcement learning framework designed for training **reasoning-and-searching interleaved LLMs**—language models that learn to reason and make tool calls (e.g., to search engines) in a coordinated manner.

<!-- It can be seen as an extension of <strong>DeepSeek-R1(-Zero)</strong> with interleaved search engine calling and an opensource RL training-based solution for <strong>OpenAI DeepResearch</strong>. -->
Built upon [veRL](https://github.com/volcengine/verl), Search-R1 extends the ideas of **DeepSeek-R1(-Zero)** by incorporating interleaved search engine access and provides a fully open-source RL training pipeline. It serves as an alternative and open solution to **OpenAI DeepResearch**, enabling research and development in tool-augmented LLM reasoning.

<!-- Through RL (rule-based outcome reward), the 3B **base** LLM (both Qwen2.5-3b-base and Llama3.2-3b-base) develops reasoning and search engine calling abilities all on its own. -->

We support different RL methods (e.g., PPO, GRPO, reinforce), different LLMs (e.g., llama3, Qwen2.5, etc) and different search engines (e.g., local sparse/dense retrievers and online search engines).

Paper: [link1](https://arxiv.org/pdf/2503.09516), [link2](https://arxiv.org/abs/2505.15117); Model and data: [link](https://huggingface.co/collections/PeterJinGo/search-r1-67d1a021202731cb065740f5); Twitter thread: [link](https://x.com/BowenJin13/status/1895544294473109889); Full experiment log: [prelim](https://wandb.ai/peterjin/Search-R1-open); [v0.1](https://wandb.ai/peterjin/Search-R1-nq_hotpotqa_train); [v0.2](https://wandb.ai/peterjin/Search-R1-v0.2); [v0.3](https://wandb.ai/peterjin/Search-R1-v0.3). Details about these logs and methods can be find [here](https://github.com/PeterGriffinJin/Search-R1/blob/main/docs/experiment_log.md).


![single-turn](public/main.png)

## News

- [2025.10] Search-R1 is featured by Thinking Machines Lab's first product [Tinker](https://github.com/thinking-machines-lab/tinker-cookbook)! Details: [Document](https://github.com/thinking-machines-lab/tinker-cookbook/tree/main/tinker_cookbook/recipes/tool_use/search).
- [2025.7] Search-R1 is supported by [SkyRL](https://github.com/NovaSky-AI/SkyRL)! Detailed instructions: [code](https://github.com/NovaSky-AI/SkyRL/tree/main/skyrl-train/examples/search), [Document](https://novasky-ai.notion.site/skyrl-searchr1).
- [2025.6] Search-R1 is now integrated into the latest version of veRL and can take advantage of its most up-to-date features! Detailed instructions: [veRL](https://verl.readthedocs.io/en/latest/sglang_multiturn/search_tool_example.html), [English Document](https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial/blob/main/rlhf/verl/multi-turn/tool_examples/verl-multiturn-searchR1-like.md), [Chinese Document](https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial/blob/main/rlhf/verl/multi-turn/tool_examples/verl-multiturn-searchR1-like_ZH.md).
- [2025.5] The second [paper](https://arxiv.org/abs/2505.15117) conducting detailed empirical studies is published with logs: [v0.3](https://wandb.ai/peterjin/Search-R1-v0.3). 
- [2025.4] We support [multinode](https://github.com/PeterGriffinJin/Search-R1/blob/main/docs/multinode.md) training for 30B+ LLMs!
- [2025.4] We support [different search engines](https://github.com/PeterGriffinJin/Search-R1/blob/main/docs/retriever.md) including sparse local retriever, dense local retriever with ANN indexing and online search engines!
- [2025.3] The first Search-R1 [paper](https://arxiv.org/pdf/2503.09516) is published with the logs: [v0.1](https://wandb.ai/peterjin/Search-R1-nq_hotpotqa_train); [v0.2](https://wandb.ai/peterjin/Search-R1-v0.2).
- [2025.2] We opensource Search-R1 codebase with [preliminary results](https://wandb.ai/peterjin/Search-R1-open).

## Links

- [Installation](#installation)
- [Quick start](#quick-start)
- [Preliminary results](#preliminary-results)
- [Inference](#inference)
- [Use your own dataset](#use-your-own-dataset)
- [Use your own search engine](#use-your-own-search-engine)
- [Features](#features)
- [Ackowledge](#acknowledge)
- [Citations](#citations)

## Installation

### Search-r1 environment
```bash
conda create -n searchr1 python=3.9
conda activate searchr1
# install torch [or you can skip this step and let vllm to install the correct version for you]
pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu121
# install vllm
pip3 install vllm==0.6.3 # or you can install 0.5.4, 0.4.2 and 0.3.1

# verl
pip install -e .

# flash attention 2
pip3 install flash-attn --no-build-isolation
pip install wandb
```

### Retriever environment (optional)
If you would like to call a local retriever as the search engine, you can install the environment as follows. (We recommend using a seperate environment.)
```bash
conda create -n retriever python=3.10
conda activate retriever

# we recommend installing torch with conda for faiss-gpu
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.1 -c pytorch -c nvidia
pip install transformers datasets pyserini

## install the gpu version faiss to guarantee efficient RL rollout
conda install -c pytorch -c nvidia faiss-gpu=1.8.0

## API function
pip install uvicorn fastapi
```


## Quick start

Train a reasoning + search LLM on NQ dataset with e5 as the retriever and wikipedia as the corpus.

(1) Download the indexing and corpus.
```bash
save_path=/the/path/to/save
python scripts/download.py --save_path $save_path
cat $save_path/part_* > $save_path/e5_Flat.index
gzip -d $save_path/wiki-18.jsonl.gz
```

(2) Process the NQ dataset.
```bash
python scripts/data_process/nq_search.py
```

(3) Launch a local retrieval server.
```bash
conda activate retriever
bash retrieval_launch.sh
```

(4) Run RL training (PPO) with Llama-3.2-3b-base.
```bash
conda activate searchr1
bash train_ppo.sh
```

## Preliminary results

(1) The base model (llama3.2-3b-base) learns to call the search engine and obtain improved performance.

![llama-3b](public/llama32-3b.png)


(2) The base model (Qwen2.5-7b-base) can learn to conduct multi-turn search engine calling and reasoning with RL.

![multi-turn](public/multi-turn.png)

## Inference
#### You can play with the trained Search-R1 model with your own question.
(1) Launch a local retrieval server.
```bash
conda activate retriever
bash retrieval_launch.sh
```

(2) Run inference.
```bash
conda activate searchr1
python infer.py
```
You can modify the ```question``` on line 7 to something you're interested in.

## Use your own dataset

### QA data
For each question-answer sample, it should be a dictionary containing the desired content as below:

```
data = {
        "data_source": data_source,
        "prompt": [{
            "role": "user",
            "content": question,
        }],
        "ability": "fact-reasoning",
        "reward_model": {
            "style": "rule",
            "ground_truth": solution
        },
        "extra_info": {
            'split': split,
            'index': idx,
        }
    }
```

You can refer to ```scripts/data_process/nq_search.py``` for a concrete data processing example.

### Corpora

It is recommended to make your corpus a jsonl file, where each line (a dictionary with "id" key and "contents" key) corresponds to one passage. You can refer to ```example/corpus.jsonl``` for an example.

The "id" key corresponds to the passage id, while the "contents" key corresponds to the passage content ('"' + title + '"\n' + text).
For example:
```
{"id": "0", "contents": "Evan Morris Evan L. Morris (January 26, 1977 \u2013 July 9, 2015) was a lobbyist for Genentech and its parent corporation Roche in Washington."}
...
{"id": "100", "contents": "Three years later, when the United States Exploring Expedition to little-known portions of the globe was organised under Charles Wilkes, Hale was recommended, while yet an undergraduate."}
...
```

**Index your corpora (optional).**
If you would like to use a local retriever as the search engine, you can index your own corpus by:
```
bash search_r1/search/build_index.sh
```
You can change ```retriever_name``` and ```retriever_model``` to your interested off-the-shelf retriever.

## Use your own search engine

Our codebase supports local sparse retriever (e.g., BM25), local dense retriever (both flat indexing with GPUs and ANN indexing with CPUs) and online search engine (e.g., Google, Bing, etc). More details can be found [here](https://github.com/PeterGriffinJin/Search-R1/tree/main/docs/retriever.md).

The main philosophy is to launch a local or remote search engine server separately from the main RL training pipeline. 

The LLM can call the search engine by calling the search API (e.g., "http://127.0.0.1:8000/retrieve").

You can refer to ```search_r1/search/retriever_server.py``` for an example of launching a local retriever server.

## Features
- Support local sparse retrievers (e.g., BM25). ✔️
- Support local dense retrievers (both flat indexing and ANN indexing) ✔️
- Support google search / bing search / brave search API and others. ✔️
- Support off-the-shelf neural rerankers. ✔️
- Support different RL methods (e.g., PPO, GRPO, reinforce). ✔️
- Support different LLMs (e.g., llama3, Qwen2.5, etc). ✔️

## Acknowledge

The concept of Search-R1 is inspired by [Deepseek-R1](https://github.com/deepseek-ai/DeepSeek-R1) and [TinyZero](https://github.com/Jiayi-Pan/TinyZero/tree/main).
Its implementation is built upon [veRL](https://github.com/volcengine/verl) and [RAGEN](https://github.com/ZihanWang314/RAGEN/tree/main). 
We sincerely appreciate the efforts of these teams for their contributions to open-source research and development.

## Awesome work powered or inspired by Search-R1

- [DeepResearcher](https://github.com/GAIR-NLP/DeepResearcher): Scaling Deep Research via Reinforcement Learning in Real-world Environments. [![[code]](https://img.shields.io/github/stars/GAIR-NLP/DeepResearcher)](https://github.com/GAIR-NLP/DeepResearcher)
- [Multimodal-Search-R1](https://github.com/EvolvingLMMs-Lab/multimodal-search-r1): Incentivizing LMMs to Search. [![[code]](https://img.shields.io/github/stars/EvolvingLMMs-Lab/multimodal-search-r1)](https://github.com/EvolvingLMMs-Lab/multimodal-search-r1)
- [OTC](https://arxiv.org/pdf/2504.14870): Optimal Tool Calls via Reinforcement Learning.
- [ZeroSearch](https://github.com/Alibaba-NLP/ZeroSearch): Incentivize the Search Capability of LLMs without Searching. [![[code]](https://img.shields.io/github/stars/Alibaba-NLP/ZeroSearch)](https://github.com/Alibaba-NLP/ZeroSearch)
- [IKEA](https://github.com/hzy312/knowledge-r1): Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search Agent. [![[code]](https://img.shields.io/github/stars/hzy312/knowledge-r1)](https://github.com/hzy312/knowledge-r1)
- [Scent of Knowledge](https://arxiv.org/abs/2505.09316): Optimizing Search-Enhanced Reasoning with Information Foraging.
- [AutoRefine](https://www.arxiv.org/pdf/2505.11277): Search and Refine During Think. [![[code]](https://img.shields.io/github/stars/syr-cn/AutoRefine)](https://github.com/syr-cn/AutoRefine)
- [O^2-Searcher](https://arxiv.org/pdf/2505.16582): A Searching-based Agent Model for Open-Domain Open-Ended Question Answering. [![[code]](https://img.shields.io/github/stars/Acade-Mate/O2-Searcher)](https://github.com/Acade-Mate/O2-Searcher)
- [MaskSearch](https://arxiv.org/pdf/2505.20285): A Universal Pre-Training Framework to Enhance Agentic Search Capability. [![[code]](https://img.shields.io/github/stars/Alibaba-NLP/MaskSearch)](https://github.com/Alibaba-NLP/MaskSearch)
- [VRAG-RL](https://arxiv.org/abs/2505.22019): Vision-Perception-Based RAG for Visually Rich Information Understanding. [![[code]](https://img.shields.io/github/stars/Alibaba-NLP/VRAG)](https://github.com/Alibaba-NLP/VRAG)
- [R1-Code-Interpreter](https://arxiv.org/abs/2505.21668): Training LLMs to Reason with Code via SFT and RL. [![[code]](https://img.shields.io/github/stars/yongchao98/R1-Code-Interpreter)](https://github.com/yongchao98/R1-Code-Interpreter)
- [R-Search](https://arxiv.org/abs/2506.04185): Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning. [![[code]](https://img.shields.io/github/stars/QingFei1/R-Search)](https://github.com/QingFei1/R-Search)
- [StepSearch](https://arxiv.org/pdf/2505.15107): Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization. [![[code]](https://img.shields.io/github/stars/Zillwang/StepSearch)](https://github.com/Zillwang/StepSearch)
- [SimpleTIR](https://simpletir.notion.site/report): Stable End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning. [![[code]](https://img.shields.io/github/stars/ltzheng/SimpleTIR)](https://github.com/ltzheng/SimpleTIR)
- [Router-R1](https://arxiv.org/pdf/2506.09033): Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning. [![[code]](https://img.shields.io/github/stars/ulab-uiuc/Router-R1)](https://github.com/ulab-uiuc/Router-R1)
- [SkyRL](https://skyrl.readthedocs.io/en/latest/): A Modular Full-stack RL Library for LLMs. [![[code]](https://img.shields.io/github/stars/NovaSky-AI/SkyRL)](https://github.com/NovaSky-AI/SkyRL)
- [ASearcher](https://arxiv.org/abs/2508.07976): Large-Scale RL for Search Agents. [![[code]](https://img.shields.io/github/stars/inclusionAI/ASearcher)](https://github.com/inclusionAI/ASearcher)
- [ParallelSearch](https://www.arxiv.org/abs/2508.09303): Decompose Query and Search Sub-queries in Parallel with RL. [![[code]](https://img.shields.io/github/stars/Tree-Shu-Zhao/ParallelSearch)](https://github.com/Tree-Shu-Zhao/ParallelSearch)
- [AutoTIR](https://arxiv.org/pdf/2507.21836): Autonomous Tools Integrated Reasoning via Reinforcement Learning. [![[code]](https://img.shields.io/github/stars/weiyifan1023/AutoTIR)](https://github.com/weiyifan1023/AutoTIR)
- [verl-tool](https://arxiv.org/pdf/2509.01055): A version of verl to support diverse tool use. [![[code]](https://img.shields.io/github/stars/TIGER-AI-Lab/verl-tool)](https://github.com/TIGER-AI-Lab/verl-tool)
- [Tree-GRPO](https://arxiv.org/abs/2509.21240): Tree Search for LLM Agent Reinforcement Learning. [![[code]](https://img.shields.io/github/stars/AMAP-ML/Tree-GRPO)](https://github.com/AMAP-ML/Tree-GRPO)
- [EviNote-RAG](https://arxiv.org/abs/2509.00877): Enhancing RAG Models via Answer-Supportive Evidence Notes. [![[code]](https://img.shields.io/github/stars/Da1yuqin/EviNoteRAG)](https://github.com/Da1yuqin/EviNoteRAG)
- [GlobalRAG](https://arxiv.org/pdf/2510.20548v1): GlobalRAG: Enhancing Global Reasoning in Multi-hop Question Answering via Reinforcement Learning. [![[code]](https://img.shields.io/github/stars/CarnegieBin/GlobalRAG)](https://github.com/CarnegieBin/GlobalRAG)





## Citations

```bibtex
@article{jin2025search,
  title={Search-r1: Training llms to reason and leverage search engines with reinforcement learning},
  author={Jin, Bowen and Zeng, Hansi and Yue, Zhenrui and Yoon, Jinsung and Arik, Sercan and Wang, Dong and Zamani, Hamed and Han, Jiawei},
  journal={arXiv preprint arXiv:2503.09516},
  year={2025}
}
```

```bibtex
@article{jin2025empirical,
  title={An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM Agents},
  author={Jin, Bowen and Yoon, Jinsung and Kargupta, Priyanka and Arik, Sercan O and Han, Jiawei},
  journal={arXiv preprint arXiv:2505.15117},
  year={2025}
}
```