jina-reranker-v3.5: Efficient Listwise Reranker with Hybrid Attention and Self-Distillation
Jina AI has released jina-reranker-v3.5, a 0.6B-parameter listwise reranker that achieves 63.20 nDCG@10 on the BEIR benchmark, matching the performance of a 4B-parameter model with significantly fewer parameters. The model introduces a hybrid attention mechanism combining sliding-window and global layers, and employs a three-stage self-distillation process to enhance efficiency and domain robustness. Notably, it delivers a 9.6-point improvement in nDCG@10 over its predecessor on semi-structured retrieval tasks and reduces inference latency by up to 1.56x.
Why it matters: This work demonstrates that efficient listwise reranking models can achieve state-of-the-art performance with far fewer parameters, enabling more cost-effective and scalable deployment in retrieval systems.
Full story at: arXiv Information Retrieval ↗