Alibaba-NLP/ERank-4B

ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking

Arxiv

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.

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	4B	36	32K	Yes
ERank-14B	14B	40	128K	Yes
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.

For more details, please refer to our Paper.

Usage

We have implemented the inference code based on Transformer and vLLM, respectively.

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}, 
}