LightOn-rerank-LW

📝 Blog post

About the LightOn-rerank family

Production retrieval pipelines usually need two rerankers: one for text passages and one for visual documents (PDF pages, slides, scans). LightOn-rerank models are unified cross-encoder rerankers: a single model scores both text passages and document page images against a query, on top of any first-stage retriever (BM25, dense embeddings, or ColPali-family late-interaction models).

The models are built on Qwen3.5 backbone (hybrid linear + full attention) and jointly fine-tuned on text and visual reranking data with mixed-modality batches (LoRA, merged into the released weights). Training data is English-only; French performance transfers zero-shot from the multilingual backbone.

The family comes in two scoring flavours × three sizes (0.8B / 2B / 4B):

• PW (pointwise): each candidate is scored independently. The model judges whether the document answers the query, and the score is logit("Yes") − logit("No"). One forward pass per candidate and no generation.
• LW (listwise): generative listwise ranking, where 4 candidates are placed in a single prompt and the model generates a permutation ([2] > [4] > [1] > [3]). Larger candidate pools are ranked with a sliding window (window 4, stride 2, bottom-to-top). Cross-document attention makes LW markedly stronger on hard visual reranking, and unlike pointwise scoring it keeps improving with backbone size.

LightOn-rerank-LW-2B is the best 2B-class model of the family: 62.66 nDCG@10 on ViDoRe V3, improving over the ColQwen2.5 first stage by 7.1 nDCG points and leading every open 2B-class multimodal reranker we measured. Putting 4 candidates in a single forward pass lets documents attend to each other, which fixes the discriminative weakness of independent per-candidate scoring: +2.8 nDCG@10 over the pointwise sibling with identical backbone and data, winning 13 of 16 splits.

Results

ViDoRe V3 (visual document reranking, 8 domains × EN/FR queries), overall nDCG@10, ColQwen2.5-v0.2 first stage, retrieve 100 / rerank 100. All models, including baselines, were re-evaluated under this same two-stage protocol, so numbers are mutually comparable but not comparable to vendor-reported end-to-end results.

ViDoRe V3 detail (nDCG@10, ColQwen2.5 first stage, rerank-100, sliding window 4/2)

Overall nDCG@10: 62.66 (EN 64.01 / FR 61.31), the best 2B-class result under this protocol: +3.5 over the official Qwen3-VL-Reranker-2B (59.18) and +3.3 over jina-reranker-m0 (59.40). The training data is English-only, so the FR column is pure zero-shot cross-lingual transfer; FR even beats EN on three domains (finance_fr, energy, physics).

BEIR results (text reranking)

13 datasets, nDCG@10, BM25 first stage, retrieve 100 / rerank 100, all models re-evaluated under the same protocol (this model runs the same W=4 stride=2 sliding window it uses for document pages; baselines score through their native text paths). ⚠️ marks datasets in the text training mix (NQ, MSMARCO); the clean mean excludes them.

The ordering flips relative to ViDoRe V3: on vision this model leads the 2B class; on text jina-reranker-m0 leads decisively, and this model is roughly tied with Qwen3-VL-Reranker-2B. On text the pointwise sibling is slightly stronger (PW-2B: 49.13 clean mean) — the mirror image of vision, where listwise wins at every size.

Model Details

• Model type: multimodal cross-encoder reranker (generative listwise: 4 candidates per prompt, ranked by generating a permutation; sliding window (4, stride 2) for larger pools)
• Base model: Qwen/Qwen3.5-2B (Qwen3.5 hybrid linear + full attention VLM)
• Parameters: ≈2.2B (bfloat16, 4.4 GB)