Search-R1

Search-R1: An Efficient, Scalable RL Training Framework for Reasoning & Search Engine Calling interleaved LLM based on veRL

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Search-R1 is a tool that trains large language models (LLMs) to reason and call a search engine using reinforcement learning. It is a reproduction of DeepSeek-R1 methods for training reasoning and searching interleaved LLMs, built upon veRL. Through rule-based outcome reward, the base LLM develops reasoning and search engine calling abilities independently. Users can train LLMs on their own datasets and search engines, with preliminary results showing improved performance in search engine calling and reasoning tasks.

README:

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

Search-R1 is an extension of DeepSeek-R1(-Zero) methods for training reasoning and searching (tool-call) interleaved LLMs. We built upon veRL.

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.

Paper: link; Model and data: link; Twitter thread: link; Full experiment log 1: link; Full experiment log 2: link

You can refer to this link for detailed instructions on reproducing the results from the paper.

Installation

Search-r1 environment

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.)

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

## 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.

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.

python scripts/data_process/nq_search.py

(3) Launch a local retrieval server.

conda activate retriever
bash retrieval_launch.sh

(4) Run RL training (PPO) with Llama-3.2-3b-base.

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.

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

Inference

You can play with the trained Search-R1 model with your own question.

(1) Launch a local retrieval server.

conda activate retriever
bash retrieval_launch.sh

(2) Run inference.

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. 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

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.

To do

Support google search / bing search / brave search API and others.
Support LoRA tuning.
Support supervised finetuning.
Support off-the-shelf rerankers.

Acknowledge

The concept of Search-R1 is inspired by Deepseek-R1 and TinyZero. Its implementation is built upon veRL and RAGEN. We sincerely appreciate the efforts of these teams for their contributions to open-source research and development. We thank Jinsung Yoon and Sercan Arik for insightful discussions.

Citations

@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 Wang, Dong and Zamani, Hamed and Han, Jiawei},
  journal={arXiv preprint arXiv:2503.09516},
  year={2025}
}

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Search-R1

README:

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

Links

Installation

Search-r1 environment

Retriever environment (optional)

Quick start

Preliminary results

Inference

You can play with the trained Search-R1 model with your own question.

Use your own dataset

QA data

Corpora

Use your own search engine

To do

Acknowledge

Citations

For Tasks:

For Jobs:

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