LightOn-rerank-PW

📝 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-PW-4B is the 4B pointwise member of the family. On visual reranking it ties the 2B pointwise model (59.80 vs 59.87 nDCG@10 on ViDoRe V3): direct evidence that independent Yes/No scoring is a capacity bottleneck that extra parameters cannot fix. On text BEIR, however, it posts the strongest scores in the family (50.19 decontaminated mean). For 4B-budget vision-heavy deployments, LightOn-rerank-LW-4B is +4.9 nDCG@10 at the same size.

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)

Overall nDCG@10: 59.80 (EN 62.37 / FR 57.23).

BEIR results (text reranking)

13 datasets, nDCG@10, BM25 first stage, retrieve 100 / rerank 100, same protocol as all other family members. ⚠️ marks datasets in the text training mix (NQ, MSMARCO); the decontaminated mean excludes them.

This is the best text result in the LightOn-rerank family (2B pointwise: 49.13, 4B listwise: 48.33 decontaminated mean). Pointwise plateaus with scale on vision but not on text. If your workload is text-dominant and you have the 4B budget, this is the family's strongest text reranker.

Model Details

• Model type: multimodal cross-encoder reranker (pointwise: each candidate is scored independently as logit("Yes") − logit("No"))
• Base model: Qwen/Qwen3.5-4B (Qwen3.5 hybrid linear + full attention VLM)
• Parameters: ≈4.5B (bfloat16, 9.1 GB)
• Inputs: query (text) + candidate document(s): text passage or page image
• Fine-tuning: joint text+vision LoRA (r=32, α=32, rsLoRA, merged into the released weights), mixed-modality batches (2 text + 2 vision groups per micro-batch), vision loss weight 1.3, lr 1e-4, 1 epoch (465 steps)
• Data: same 213k groups as the listwise models — 107k text groups (NQ, TriviaQA, MS MARCO; each a [pos, neg_0, neg_1, neg_2] 4-list with hard negatives mined via the NV-Retriever approach with GTE-ModernBERT) + 106k vision groups (ColPali train set with negatives mined by Nomic). For pointwise training each group is flattened into (query, document, Yes/No) triples.
• Languages: English (training), French (zero-shot transfer)
• Requirements: transformers >= 5.4.0 (qwen3_5 architecture); sentence-transformers >= 5.4.0 for the CrossEncode