# Document Reranking

RenderRank renders document text as images and encodes them into compressed visual tokens, aligning relevance scores to a text-based teacher and then refining positive-versus-negative scores within each query, reaching an average NDCG@10 of 55.96 on 11 BEIR datasets with 290.07 average input tokens, 16.5–35.5% fewer than the evaluated text-based rerankers, and an average NDCG@10 of 88.27 on four long-document datasets at roughly half their average input token count.

RenderRank renders document text as images and encodes them into compressed visual tokens, aligning relevance scores to a text-based teacher and then refining positive-versus-negative scores within each query, reaching an average NDCG@10 of 55.96 on 11 BEIR datasets with 290.07 average input tokens, 16.5–35.5% fewer than the evaluated text-based rerankers, and an average NDCG@10 of 88.27 on four long-document datasets at roughly half their average input token count.

The work proposes AdaTutoRank, a setwise reranker trained with Adaptive Tutoring Optimization (ATO) under a three-level, nine-dimension rubric hierarchy that assigns each rollout one of three hint forms—rubrics, a corrective reflection, or a sibling set—matched to its reward, and distills the hint's effect into a token-level advantage combined with the group-relative outcome advantage; across ten benchmarks spanning RAG, deep research, and setwise evaluation it attains the best overall performance while reducing the agent's retrieval calls.

The work proposes AdaTutoRank, a setwise reranker trained with Adaptive Tutoring Optimization (ATO) under a three-level, nine-dimension rubric hierarchy that assigns each rollout one of three hint forms—rubrics, a corrective reflection, or a sibling set—matched to its reward, and distills the hint's effect into a token-level advantage combined with the group-relative outcome advantage; across ten benchmarks spanning RAG, deep research, and setwise evaluation it attains the best overall performance while reducing the agent's retrieval calls.