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Model: iic/ERank-4B Source: Original Platform
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+---
+license: apache-2.0
+---
+
+<div align="center">
+<h1>ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking</h1> 
+</div>
+
+<p align="center">
+   <a href="https://arxiv.org/abs/2509.00520">Arxiv</a>
+</p>
+
+## Introduction
+
+We introduce ERANK, a highly effective and efficient pointwise reranker built from a reasoning LLM, which excels across diverse relevance scenarios with low latency.
+Surprisingly, it also outperforms recent listwise rerankers on the most challenging reasoning-intensive tasks.
+
+<img src="./assets/overview.png">
+
+ERank is trained with a novel two-stage training pipeline, i.e.,  Supervised Fine-Tuning (SFT) and Reinforcement
+Learning (RL).
+During the SFT stage, unlike traidtional pointwise rerankers that train the LLMs for binary relevance classification, we encourage the LLM to generatively output fine grained integer scores.
+In the RL training, we introduce a novel listwise derived reward, which instills global ranking awareness into the efficient
+pointwise architecture.
+
+## Model List
+
+We provide the trained reranking models in various sizes (4B, 14B and 32B), all of which support customizing the input instruction according to different tasks.
+
+| Model                                    | Size | Layers | Sequence Length | Instruction Aware |
+|------------------------------------------|------|--------|-----------------|-------------------|
+| [ERank-4B](https://huggingface.co/Alibaba-NLP/ERank-4B)      | 4B   | 36     | 32K             | Yes               |
+| [ERank-14B](https://huggingface.co/Alibaba-NLP/ERank-14B)     | 14B  | 40     | 128K            | Yes               |
+| [ERank-32B](https://huggingface.co/Alibaba-NLP/ERank-32B)     | 32B  | 64     | 128K            | Yes               |
+
+## Evaluation
+
+We evaluate ERank on both reasoning-intensive benchmarks (BRIGHT and FollowIR) and traditional semantic relevance benchmarks (BEIR and TREC DL).
+All methods use the original queries without hybrid scores.
+
+| Paradigm | Method | Average | BRIGHT | FollowIR | BEIR | TREC DL |
+| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
+| - | First-stage retriever | 25.9 | 13.7 | 0 | 40.8 | 49.3 |
+| Listwise | Rank-R1-7B | 34.6 | 15.7 | 3.6 | **49.0** | 70.0 |
+| Listwise | Rearank-7B | 35.3 | 17.4 | 2.3 | **49.0** | **72.5** |
+| Pointwise | JudgeRank-8B | 32.1 | 17.0 | 9.9 | 39.1 | 62.6 |
+| Pointwise | Rank1-7B | 34.6 | 18.2 | 9.1 | 44.2 | 67.1 |
+| Pointwise | **ERank-4B (Ours)** | 36.8 | 22.7 | 11.0 | 44.8 | 68.9 |
+| Pointwise | **ERank-14B (Ours)** | 36.9 | 23.1 | 10.3 | 47.1 | 67.1 |
+| Pointwise | **ERank-32B (Ours)** | **38.1** | **24.4** | **12.1** | 47.7 | 68.1 |
+
+On the most challenging BRIGHT benchmark, with top-100 documents retrieved by ReasonIR-8B using GPT-4 reason-query, ERank with BM25 hybrid achieves the state-of-the-art NDCG@10.
+
+| Method | nDCG@10 |
+| :--- | :--- |
+| ReasonIR-8B | 30.5 |
+| Rank-R1-7B | 24.1 |
+| Rank1-7B | 24.3 |
+| Rearank-7B | 27.5 |
+| JudgeRank-8B | 20.2 |
+| *+ BM25 hybrid* | 22.7 |
+| Rank-R1-32B-v0.2 | 37.7 |
+| *+ BM25 hybrid* | 40.0 |
+| **ERank-4B (Ours)** | 30.5 |
+| *+ BM25 hybrid* | 38.7 |
+| **ERank-14B (Ours)** | 31.8 |
+| *+ BM25 hybrid* | 39.3 |
+| **ERank-32B (Ours)** | 32.8 |
+| *+ BM25 hybrid* | **40.2** |
+
+Since ERank is a pointwise reranker, it has low latency compared with listwise models.
+
+<div align="center">
+<img src="./assets/latency.png" width=400px>
+</div>
+
+For more details, please refer to our [Paper](https://arxiv.org/abs/2509.00520).
+
+## Usage
+
+We have implemented the inference code based on Transformer and vLLM, respectively.
+
+```python
+from examples.ERank_Transformer import ERank_Transformer
+from examples.ERank_vLLM import ERank_vLLM
+from examples.utils import hybrid_scores
+
+# select a model
+model_name_or_path = "Alibaba-NLP/ERank-4B"
+# model_name_or_path = "Alibaba-NLP/ERank-14B"
+# model_name_or_path = "Alibaba-NLP/ERank-32B"
+
+# use vLLM or Transformer
+# reranker = ERank_Transformer(model_name_or_path)
+reranker = ERank_vLLM(model_name_or_path)
+
+# input data
+instruction = "Retrieve relevant documents for the query."
+query = "I am happy"
+docs = [
+   {"content": "excited", "first_stage_score": 46.7},
+   {"content": "sad", "first_stage_score": 1.5},
+   {"content": "peaceful", "first_stage_score": 2.3},
+]
+
+# rerank
+results = reranker.rerank(query, docs, instruction, truncate_length=2048)
+print(results)
+# [
+#	 {'content': 'excited', 'first_stage_score': 46.7, 'rank_score': 4.84}, 
+#	 {'content': 'peaceful', 'first_stage_score': 2.3, 'rank_score': 2.98}
+#	 {'content': 'sad', 'first_stage_score': 1.5, 'rank_score': 0.0}, 
+# ]
+
+# Optional: hybrid with first-stage scores
+alpha = 0.2
+hybrid_results = hybrid_scores(results, alpha)
+print(hybrid_results)
+# [
+#	 {'content': 'excited', 'first_stage_score': 46.7, 'rank_score': 4.84, 'hybrid_score': 1.18}, 
+#	 {'content': 'peaceful', 'first_stage_score': 2.3, 'rank_score': 2.98, 'hybrid_score':0.01}, 
+#	 {'content': 'sad', 'first_stage_score': 1.5, 'rank_score': 0.0, 'hybrid_score': -1.19}
+# ]
+```
+
+Please refer to the `examples` directory for details, in which we also provide the instructions used in the prompt during evaluation.
+
+
+## Citation
+If you find our work helpful, feel free to give us a cite.
+
+```
+@misc{ERank,
+      title={ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking}, 
+      author={Yuzheng Cai and Yanzhao Zhang and Dingkun Long and Mingxin Li and Pengjun Xie and Weiguo Zheng},
+      year={2025},
+      eprint={2509.00520},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2509.00520}, 
+}
+```