README.md

---
base_model:
- Qwen/Qwen2.5-7B-Instruct
datasets:
- liuwenhan/reasonrank_data_sft
- liuwenhan/reasonrank_data_rl
- liuwenhan/reasonrank_data_13k
language:
- en
license: mit
pipeline_tag: text-ranking
library_name: transformers
tags:
- qwen
- reranker
- passage-ranking
---

# ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability

<p align="center">
  <a href="https://arxiv.org/pdf/2508.07050" target="_blank"><img src="https://img.shields.io/badge/Paper-arXiv-b5212f.svg?logo=arxiv"></a>
  <a href="https://github.com/8421BCD/ReasonRank" target="_blank"><img src="https://img.shields.io/badge/GitHub-Repo-181717.svg?logo=github"></a>
  <a href="https://brightbenchmark.github.io/" target="_blank"><img src="https://img.shields.io/badge/Project%20Page-BRIGHT-blue.svg"></a>
  <a href="https://opensource.org/licenses/MIT"><img alt="License" src="https://img.shields.io/badge/LICENSE-MIT-green.svg"></a>
</p>

<p align="center">
🤗 <a href="https://huggingface.co/liuwenhan/reasonrank-7B" target="_blank">reasonrank-7B</a> ｜
🤗 <a href="https://huggingface.co/liuwenhan/reasonrank-32B" target="_blank">reasonrank-32B</a>
  </p>
<p align="center">
🤗 <a href="https://huggingface.co/datasets/liuwenhan/reasonrank_data_13k" target="_blank">reasonrank_data_13k</a> ｜
🤗 <a href="https://huggingface.co/datasets/liuwenhan/reasonrank_data_sft" target="_blank">reasonrank_data_sft</a> ｜
🤗 <a href="https://huggingface.co/datasets/liuwenhan/reasonrank_data_rl" target="_blank">reasonrank_data_rl</a>
</p>
<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub.</h5>

## 📣 Latest News
- **[Aug 9, 2025]**: 🏆 Our ReasonRank (32B) has achieved **SOTA performance 40.8** on **[BRIGHT leaderboard](https://brightbenchmark.github.io/)**!
- **[Aug 9, 2025]**: 📄 We uploaded our paper to the **[arXiv](https://arxiv.org/pdf/2508.07050)** and **[Hugging Face](https://huggingface.co/papers/2508.07050)**.
- **[Aug 9, 2025]**: 🔥 We released our **[🤗full reasonrank training data (13k)](https://huggingface.co/datasets/liuwenhan/reasonrank_data_13k)**, **[🤗cold-start SFT data](https://huggingface.co/datasets/liuwenhan/reasonrank_data_sft)** and **[🤗RL data](https://huggingface.co/datasets/liuwenhan/reasonrank_data_rl)**.
- **[Aug 9, 2025]**: 🔥 We released our reasoning-intensive reranker **[🤗reasonrank-7B](https://huggingface.co/liuwenhan/reasonrank-7B)** and **[🤗reasonrank-32B](https://huggingface.co/liuwenhan/reasonrank-32B)**.
- **[Aug 9, 2025]**: 🚀 We released our full codebase, including inference, SFT training, and RL training.

## 1. ReasonRank

### 💡 1.1 Overview

**ReasonRank** is a **reasoning-intensive passage reranker** tailored for reasoning-intensive ranking tasks. To train it, we first design an automated reasoning-intensive training data synthesis framework and synthesize 1.3k high-quality training data.

<p align="center">
<img width="80%" alt="image" src="https://8421bcd.oss-cn-beijing.aliyuncs.com/img/image-20250809002302377.png" />
</p>

Based on the training data, we design a two-stage training approach including **cold-start SFT** and **multi-view ranking reward RL** to inject listwise ranking ability to our ReasonRank.

<p align="center">
<img width="80%" alt="image" src="https://8421bcd.oss-cn-beijing.aliyuncs.com/img/image-20250809002546838.png" />
</p>

### 📊 1.2 Overall Performance

When using ReasonIR as initial passage retriever, our ReasonRank demonstrates strong overall ranking performance on BRIGHT benchmark, while showing superior efficiency compared with pointwise reasoning-intensive reranker Rank1.

<p align="center">
<img width="50%" alt="image" src="https://8421bcd.oss-cn-beijing.aliyuncs.com/img/image-20250809003636871.png" />
</p>

Besides, when using a higher-quality retrieval results (RaDeR + BM25 hybrid, provided by [RaDeR](https://github.com/Debrup-61/RaDeR/blob/main/BRIGHT_score_files/RaDeR-gte-Qwen2-LLMq_CoT_lexical/aops/hybrid_BM25_Rader.json)), our ReasonRank (32B) achieves SOTA performance **40.8** on [BRIGHT leaderboard](https://brightbenchmark.github.io/).

## 📂 2. The Introduction of ReasonRank Training Data

An important contribution of our work is our reasoning-intensive training data ([reasonrank_data_13k](https://huggingface.co/datasets/liuwenhan/reasonrank_data_13k)). The dataset fields of ``training_data_all.jsonl`` are as follows:

#### **Dataset Fields & Descriptions**

1. **`dataset`** *(str)*
   - The dataset name of each piece of data (e.g., `"math-qa"`).
2. **`qid`** *(str)*
   - The query ID. The content is provided in ``id_query/`` directory.
3. **`initial_list`** *(List[str])*
   - The initial list of passage IDs before DeepSeek-R1 reranking. The content of each passage ID is provided in ``id_doc/`` directory.
4. **`final_list`** *(List[str])*
   - The re-ranked list of passage IDs after listwisely reranking with DeepSeek-R1.
   - Reflects the improved ranking based on reasoning-enhanced relevance scoring.
5. **`reasoning`** *(str)*
   - A  **step-by-step reasoning chain** outputted by DeepSeek-R1 while performing the listwise reranking.
6. **`relevant_docids`** *(List[str])*
   - The ids of relevant passages in ``initial_list`` mined by DeepSeek-R1. The remaining passage ids in ``initial_list`` are irrelevant ones. 
   - Note that **`relevant_docids`** are not necessarily ranked at the top of **`final_list`** by the DeepSeek-R1, which may stem from inconsistencies in DeepSeek-R1’s judgments. To address this, you can apply the **self-consistency data filtering** technique proposed in our paper to select higher-quality data.

The statistics of dataset is shown in the figure below:
<p align="center">
<img width="80%" alt="image" src="https://github.com/user-attachments/assets/c04b9d1a-2f21-46f1-b23d-ad1f50d22fb8" />
</p>

#### **Example Entry**

```json
{
  "dataset": "math-qa",
  "qid": "math_1001",
  "initial_list": ["math_test_intermediate_algebra_808", "math_train_intermediate_algebra_1471", ...],
  "final_list": ["math_test_intermediate_algebra_808", "math_test_intermediate_algebra_1678", ...],
  "reasoning": "Okay, I need to rank the 20 passages based on their relevance...",
  "relevant_docids": ["math_test_intermediate_algebra_808", "math_train_intermediate_algebra_1471", "math_train_intermediate_algebra_993"]
}
```

#### **Application**

1. Training passage reranker: Given the reranked passage list, one can use our data to train a listwise reranker
2. Training passage retriever: Using the **`relevant_docids`** and the remaining irrelevant ids, one can train a passage retriever.

## ⚡ 3. Quick Start

This section provides a general guide on how to use ReasonRank for inference. For detailed environment setup, specific inference commands (including usage with ReasonIR or custom retrieval results), and in-depth training procedures (Cold-Start SFT, Multi-reward ranking RL), please refer to the [official GitHub repository](https://github.com/8421BCD/ReasonRank).

## Sample Usage

This model can be loaded and used with the `transformers` library. Below is a basic example demonstrating how to use the model for passage re-ranking. The model expects a specific chat-like format for input, including a system prompt and a user query with listed passages.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "liuwenhan/reasonrank-7B" # Or "liuwenhan/reasonrank-32B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
).eval()

# System prompt as used in the paper for reasoning-intensive ranking
system_prompt = (
    "You are a helpful and harmless AI assistant. You will be provided with a search query and a list of passages, "
    "and your task is to re-rank the passages based on their relevance to the query. "
    "You should follow a chain of thought to determine the most relevant passages. "
    "Your final answer should be a list of the re-ranked passage numbers, separated by commas. "
    "Do not include any other information or explanation in your final answer."
)

query = "What is the capital of France?"
passages = [
    "Paris is the capital and most populous city of France.",
    "The Eiffel Tower is a famous landmark in Paris.",
    "France is a country located in Western Europe.",
    "London is the capital of the United Kingdom."
]

# Construct the user message with query and passages
user_content = f"Search Query: {query}
"
for i, passage in enumerate(passages):
    user_content += f"[{i+1}] {passage}
"
user_content += "Please re-rank the passages based on their relevance to the query. Provide a chain of thought and then the final re-ranked list."

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_content}
]

# Apply chat template
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Tokenize input
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(model.device)

# Generate response
output_ids = model.generate(
    input_ids,
    max_new_tokens=256, # Adjust as needed for reasoning length
    do_sample=False,    # Typically deterministic for ranking/reasoning
    temperature=0.1,    # Low temperature for focused output
    repetition_penalty=1.05,
    eos_token_id=tokenizer.eos_token_id
)

# Decode output
generated_text = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
print(generated_text)
```

## Citation

If you find this work helpful, please cite our papers:

```bibtex
@misc{liu2025reasonrankempoweringpassageranking,
      title={ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability}, 
      author={Wenhan Liu and Xinyu Ma and Weiwei Sun and Yutao Zhu and Yuchen Li and Dawei Yin and Zhicheng Dou},
      year={2025},
      eprint={2508.07050},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2508.07050}, 
}
```

## 🤝 Acknowledge

The inference codes and training implementation build upon [RankLLM](https://github.com/castorini/rank_llm), [Llama Factory](https://github.com/hiyouga/LLaMA-Factory) and [verl](https://github.com/volcengine/verl). Our work is based on the [Qwen2.5](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) model series, and we sincerely thank the Qwen team for their outstanding contributions to the open-source community.

## 📄 License

This project is released under the [MIT License](LICENSE).

## 📞 Contact

For any questions or feedback, please reach out to us at [[email protected]]([email protected]).

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=8421bcd/reasonrank&type=Date)](https://www.star-history.com/#8421bcd/reasonrank&Date)