# Rank4Gen: RAG-Preference-Aligned Document Set Selection and Ranking

arXiv: https://arxiv.org/abs/2601.11273 · HTML: https://mlsanity.com/paper/2601.11273/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2026.

## Built on by (most cited first, 4 of 4)

- [Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking](https://mlsanity.com/paper/2607.19747/) (2026)
- [Training Documents Reranker with Search Rubrics for Deep Research Agent](https://mlsanity.com/paper/2608.03527/) (2026)
- [AdaTutoRank: Learning to Rerank Document Sets via Adaptive Tutoring Optimization for RAG and Deep Research](https://mlsanity.com/paper/2609.32472/) (2026)
- [Preference Is Not Intervention: The Structure and Stability Boundaries of Reader-Specific Evidence Utility](https://mlsanity.com/paper/2608.17781/) (2026)

## Abstract (arXiv)

In the RAG paradigm, document ranking determines the evidence available to downstream generators. Through controlled analysis, we identify two phenomena underexplored by existing rankers: (i) downstream response quality depends not only on relevance but also on the composition and ordering of selected documents, and (ii) such preferences differ systematically across generators. However, existing rankers are trained purely on query--document relevance, leaving both phenomena unmodeled. To close this gap, we construct PRISM, a bilingual preference-aligned dataset built through a four-stage pipeline that compresses the combinatorial subset-and-ordering space by roughly four orders of magnitude and produces response-quality preference supervision conditioned on seven downstream generators. On a 13k-query subset of PRISM, we train Rank4Gen, a generator-aware ranker that performs joint document set selection and ordering. Experiments on five challenging RAG benchmarks show that Rank4Gen improves downstream QA quality on most evaluated generators, with per-generator F1 gains of up to +2.08 over the strongest set-selection baseline. Code is available at https://github.com/JOHNNY-fans/Rank4Gen.