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Rank1: Test-Time Compute for Reranking in Information Retrieval

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arxiv 2502.18418 v2 pith:OGAI753B submitted 2025-02-25 cs.IR cs.CLcs.LG

Orion Weller , Kathryn Ricci , Eugene Yang , Andrew Yates , Dawn Lawrie , Benjamin Van Durme This is my paper · ORCID
classification cs.IRcs.CLcs.LG
keywords computemodelrank1reasoningperformancetest-timedatasetexplainable
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce Rank1, the first reranking model trained to take advantage of test-time compute. Rank1 demonstrates the applicability within retrieval of using a reasoning language model (i.e. OpenAI's o1, Deepseek's R1, etc.) for distillation in order to rapidly improve the performance of a smaller model. We gather and open-source a dataset of more than 600,000 examples of R1 reasoning traces from queries and passages in MS MARCO. Models trained on this dataset show: (1) state-of-the-art performance on advanced reasoning and instruction following datasets; (2) work remarkably well out of distribution due to the ability to respond to user-input prompts; and (3) have explainable reasoning chains that can be given to users or RAG-based systems. Further, we demonstrate that quantized versions of these models retain strong performance while using less compute/memory. Overall, Rank1 shows that test-time compute allows for a fundamentally new type of explainable and performant reranker model for search.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TabRank: Chain-of-Thought Distillation for Table Re-Rankers

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Conditioning a table reranker on teacher chain-of-thought traces during training, without supervising generation of the traces, improves out-of-distribution table retrieval accuracy over base, naive SFT, and standard ...

  2. RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents

    cs.CL 2026-05 unverdicted novelty 6.0 of 10

    RICE-PO trains retrieval agents using retrieval scores of intermediate summaries as local rewards, gated by influence and residual-stability estimates, outperforming group-based RL baselines on BRIGHT and BEIR.

  3. DeliCIR: Memory-Guided Test-Time Deliberation via Multi-Agent Collaboration for Composed Image Retrieval

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    Proposes PDF, a hierarchical multi-agent Perception-to-Deliberation Framework that adds experience self-evolution and test-time scaling to composed image retrieval, claiming SOTA on CIRR, CIRCO, and FashionIQ.

  4. Test-time Scaling over Perception: Resolving the Grounding Paradox in Thinking with Images

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    TTSP samples and filters multiple zoom-in exploration traces and iteratively consolidates validated observations into an Evidence Ledger, improving fine-grained multimodal reasoning on V* Bench, HR-Bench, TreeBench, a...

  5. Benchmarking Information Retrieval Models on Complex Retrieval Tasks

    cs.IR 2025-09 conditional novelty 6.0 of 10

    CRUMB is a new benchmark for complex, multi-aspect retrieval tasks on which state-of-the-art retrieval models score poorly, and query rewriting does not rescue the best models.

  6. ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking

    cs.IR 2025-08 conditional novelty 6.0 of 10

    ERank trains a pointwise reasoning-LLM reranker with fine-grained integer scoring and a listwise reinforcement-learning reward, reporting nDCG@10 of 40.2 on BRIGHT for its 32B variant with BM25 hybrid scoring.

  7. TongSearch-QR: Reinforced Query Reasoning for Retrieval

    cs.IR 2025-06 conditional novelty 6.0 of 10

    TongSearch-QR trains 1.5B and 7B models with GRPO and a frozen-embedding reward to rewrite queries, reaching 27.9 nDCG@10 on BRIGHT with BM25, above GPT-4o's 26.5.

  8. From Token to Action: State Machine Reasoning to Mitigate Overthinking in Information Retrieval

    cs.IR 2025-05 conditional novelty 6.0 of 10

    A state-machine framework that replaces token-level chain-of-thought with discrete query-refinement and reranking actions reduces token use by 74% while improving nDCG@10 on retrieval benchmarks.

  9. RaDeR: Reasoning-aware Dense Retrieval Models

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A math-trained dense retriever and reranker, built from MCTS reasoning trajectories and self-reflection, outperforms strong baselines on reasoning-intensive retrieval benchmarks and beats BM25 on chain-of-thought queries.

  10. Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?

    cs.IR 2025-05 conditional novelty 6.0 of 10

    Standard pointwise rerankers beat reasoning-based pointwise rerankers under identical training, and removing the reasoning process at inference partially recovers the gap.

  11. Beyond Sequential Reranking: Reranker-Guided Search Improves Reasoning Intensive Retrieval

    cs.IR 2025-09 conditional novelty 5.0 of 10

    Reranker-Guided-Search, a greedy graph search steered by reranker scores, outperforms sequential top-k reranking under a fixed budget on three reasoning-intensive retrieval benchmarks.

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