# zerosearch
**Repository Path**: itwennet/zerosearch
## Basic Information
- **Project Name**: zerosearch
- **Description**: ZeroSearch 是一种新颖的强化学习框架，它无需与真实的搜索引擎交互即可激励 LLM 的搜索能力
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: https://www.oschina.net/p/zerosearch
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 2
- **Created**: 2025-05-11
- **Last Updated**: 2025-05-11
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
ZeroSearch: Incentivize the Search Capability of LLMs without Searching
Hao Sun, Zile Qiao, Jiayan Guo, Xuanbo Fan, Yingyan Hou
Yong Jiang, Pengjun Xie, Fei Huang, Yan Zhang
Tongyi Lab 
, Alibaba Group
# 📌 Introduction
- We propose ZeroSearch, a novel reinforcement learning framework that incentivizes the search capability of LLMs without interacting with real search engines.
- Through supervised fine-tuning, we transform the LLM into a retrieval module capable of generating both relevant and noisy documents in response to a query. We further introduce a curriculum rollout mechanism to progressively elicit the model’s reasoning ability by exposing it to increasingly challenging retrieval scenarios.
- We conduct extensive experiments on both in-domain and out-of-domain datasets. Results show that ZeroSearch outperforms real search engine-based models while incurring zero API cost. Moreover, it generalizes well across both base and instruction-tuned LLMs of various sizes and supports different reinforcement learning algorithms.
# 🛠 Dependencies
```bash
conda create -n zerosearch python=3.9
conda activate zerosearch
pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu121
pip install vllm==0.6.3
pip install wandb
pip install serpapi
# verl
pip install -e .
# flash attention 2
pip3 install flash-attn --no-build-isolation
# sglang
pip install sglang
```
# 📖 Quick Start
(1) Download the training dataset.
```bash
huggingface-cli download --repo-type dataset --resume-download sunhaonlp/ZeroSearch_dataset --local-dir ZeroSearch_dataset
```
(2) Download the simulation LLMs.
```bash
# There are different parameter sizes, please choose based on your needs.
huggingface-cli download --resume-download sunhaonlp/SearchSimulation_3B --local-dir SearchSimulation_3B
huggingface-cli download --resume-download sunhaonlp/SearchSimulation_7B --local-dir SearchSimulation_7B
huggingface-cli download --resume-download sunhaonlp/SearchSimulation_14B --local-dir SearchSimulation_14B
```
(3) Launch a local simulation server.
```bash
# Prompt-based simulation
python -m sglang.launch_server --model-path Qwen2.5-14B-Instruct --host 0.0.0.0 --tp 2 --dp 2 --port 6001
# Fine-tuning-based simulation
python -m sglang.launch_server --model-path SearchSimulation_14B --host 0.0.0.0 --tp 2 --dp 2 --port 6001
```
(4) Conduct RL training with Llama-3.2-3B.
```bash
# Activate the Conda environment
conda activate zerosearch
# Set your Google Search API key
export SER_API_KEY=your_api_key
# You can run GRPO or PPO training using the scripts below. GRPO is recommended due to its greater training stability.
# The START_THRESHOLD and END_THRESHOLD parameters define the initial and final difficulty levels of the training tasks. Adjusting these values can help optimize model performance.
## Prompt-based simulation
bash train_grpo.sh NUM_GPUS_PER_NODE 4 MODEL_PATH Llama-3.2-3B DATA_PATH ZeroSearch_dataset TOTAL_STEPS 203 IP localhost SEARCH_MODE simulate_prompt SIMULATION_LLM Qwen2.5-14B-Instruct START_THRESHOLD 0.25 END_THRESHOLD 0.5
bash train_ppo.sh NUM_GPUS_PER_NODE 4 MODEL_PATH Llama-3.2-3B DATA_PATH ZeroSearch_dataset TOTAL_STEPS 203 IP localhost SEARCH_MODE simulate_prompt SIMULATION_LLM Qwen2.5-14B-Instruct START_THRESHOLD 0.25 END_THRESHOLD 0.5
## Fine-tuning-based simulation
bash train_grpo.sh NUM_GPUS_PER_NODE 4 MODEL_PATH Llama-3.2-3B DATA_PATH ZeroSearch_dataset TOTAL_STEPS 203 IP localhost SEARCH_MODE simulate_sft SIMULATION_LLM SearchSimulation_14B START_THRESHOLD 0.25 END_THRESHOLD 0.5
bash train_ppo.sh NUM_GPUS_PER_NODE 4 MODEL_PATH Llama-3.2-3B DATA_PATH ZeroSearch_dataset TOTAL_STEPS 203 IP localhost SEARCH_MODE simulate_sft SIMULATION_LLM SearchSimulation_14B START_THRESHOLD 0.25 END_THRESHOLD 0.5
```
# 💡 Performance
### 📊 Main Results
### 📊 Compare ZeroSearch with Real Search Engine
### 📊 Choice of Simulation LLMs
### 📊 Case Study
# 🙏 Acknowledgements
This work is implemented based on [Search-R1](https://github.com/PeterGriffinJin/Search-R1), [veRL](https://github.com/volcengine/verl), and [RAGEN](https://github.com/ZihanWang314/RAGEN/tree/main). We sincerely thank the authors of these projects for their valuable contributions to the open-source community.
## 🚩Citation
If this work is helpful, please kindly cite as:
```bigquery
@misc{sun2025zerosearchincentivizesearchcapability,
title={ZeroSearch: Incentivize the Search Capability of LLMs without Searching},
author={Hao Sun and Zile Qiao and Jiayan Guo and Xuanbo Fan and Yingyan Hou and Yong Jiang and Pengjun Xie and Fei Huang and Yan Zhang},
year={2025},
eprint={2505.04588},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.04588},
}
``` 
