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.
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.
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},
}
