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REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

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arxiv 2505.20046 v1 pith:B2NITSQF submitted 2025-05-26 cs.IR cs.CL

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

Le Zhang , Bo Wang , Xipeng Qiu , Siva Reddy , Aishwarya Agrawal This is my paper
classification cs.IR cs.CL
keywords rearankbenchmarkslearningreasoningreinforcementrerankingagentgpt-4
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present REARANK, a large language model (LLM)-based listwise reasoning reranking agent. REARANK explicitly reasons before reranking, significantly improving both performance and interpretability. Leveraging reinforcement learning and data augmentation, REARANK achieves substantial improvements over baseline models across popular information retrieval benchmarks, notably requiring only 179 annotated samples. Built on top of Qwen2.5-7B, our REARANK-7B demonstrates performance comparable to GPT-4 on both in-domain and out-of-domain benchmarks and even surpasses GPT-4 on reasoning-intensive BRIGHT benchmarks. These results underscore the effectiveness of our approach and highlight how reinforcement learning can enhance LLM reasoning capabilities in reranking.

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Cited by 5 Pith papers

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

  1. Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

    cs.CL 2026-04 conditional novelty 6.0

    RRPO formulates document reranking as a sequential MDP and optimizes a pointwise reranker with PPO using LLM generation rewards and a reference-anchored deterministic baseline.

  2. MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning

    cs.IR 2026-03 conditional novelty 6.0

    MemSifter trains a 4B proxy with an outcome-driven, rank-sensitive RL reward to sift LLM memory, and on eight benchmarks it matches or beats embedding, graph, and long-context baselines.

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

    cs.IR 2025-08 conditional novelty 6.0

    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.

  4. ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability

    cs.IR 2025-08 unverdicted novelty 6.0

    ReasonRank synthesizes reasoning-intensive training data using DeepSeek-R1 and applies a two-stage SFT plus RL process with a novel multi-view ranking reward to create a listwise reranker that outperforms baselines wi...

  5. Rich-Media Re-Ranker: A User Satisfaction-Driven LLM Re-ranking Framework for Rich-Media Search

    cs.IR 2026-02 unverdicted novelty 5.0

    A re-ranking system for rich-media search that plans query intents from sessions, adds visual signals from VLMs, and uses an LLM to score results on multiple facets before multi-task RL adaptation, with reported gains...