RL for IR

RL: Reinforcement Learning · IR: Information Retrieval

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4 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 19

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Beyond Outcome Rewards: Constructing and Assigning Retrieval Credit for Search Agents
Oct 7, 2026Wenyu Huang, Xinyu Hou, Pavlos Vougiouklis +2Reward Design for RL RL for IR
University of Edinburgh, UK · Huawei Technologies Research & Development (UK) Limited
Learning to Retrieve via Reinforcement Learning in Embedding Space
Oct 6, 2026Qi Liu, Fengming Liang, Yiqun Chen +2RL for IR Text Embedding Models
Renmin University of China
T-Search: An Open Agentic Retriever and Playground for Hard Multi-Step Search
Oct 5, 2026Olga Tsymboi, Ramil Latypov, Aleksandr Medvedev +5Multi-Hop QA Agentic Search
T-Tech
LexiHorizon: Stabilizing Reinforcement Learning for Long-Horizon Deep Search
Oct 4, 2026Zhiqing Nong, Liang Wen, Chao-Hsuan LiuLong-Horizon Agent Evaluation RL for IR
Learning to Route in Visual Space via Multi-Step Embedding Retrieval
Sep 30, 2026Tianyu Chen, Mingyuan Zhou, Jiaxing WuMultimodal Search Agents Agentic Retrieval
The University of Texas at Austin · Google DeepMind
Learning to Retrieve Missing Evidence for Long-Term Memory QA
Sep 29, 2026Yi-Xuan Deng, Yi Zhang, Wei Liu +2Persistent Memory for Language Models Question Answering
Tsinghua University
Search-Aware Reinforcement Learning for Multi-Component Query Understanding in Roblox Game Search
Sep 24, 2026Nayoung Choi, Shengjian Chen, Xiaokai Wei +6Reinforcement Learning RL for IR
Emory University Atlanta, Georgia, USA · Roblox Corporation San Mateo, California, USA
It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement Learning
Sep 1, 2026Runpeng Dai, Kaili Huang, Changsung Kang +1Reinforcement Learning Generative Retrieval
University of North Carolina at Chapel Hill · Apple
Learning from What You Retrieve: Online RL Fine-Tuning for Semantic Retrieval
Aug 31, 2026Shaowei Wei, Chong Huang, Songtao Fang +3Semantic Search RL for IR
Alibaba Group Hangzhou, China
SearchWiki: Learning to Build and Navigate Knowledge Wikis for Active Information Seeking
Aug 30, 2026Guransh Singh, Vishwajeet Kumar, Arkadeep Acharya +3Retrieval-Augmented Generation Agentic Retrieval
Birla Institute of Technology, Mesra · IBM
GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning
Aug 23, 2026Jun Chen, Yongchao Liu, Pengyu Qiu +6Multi-Hop QA Agentic RAG
1Shenzhen University, Shenzhen, China · 2Great Bay University, Dongguan, China · 3Ant Group, Hangzhou, China +1
Harness-G: A Graph-Structured Harness for Search Agents
Jul 30, 2026Yanning Hou, Haoyuan Chen, Sihang Zhou +7Graph Retrieval-Augmented Generation Credit Assignment in RL
National University of Defense Technology · Changsha, China
ToE: A Hierarchical and Explainable Claim Verification Framework with Dynamic Multi-source Evidence Retrieval and Aggregation
Jun 26, 2026Zhaoqi Wang, Zijian Zhang, Kun Zheng +4Evidence-Grounded Reasoning Automated Fact-Checking
School of Cyberspace Science and Technology, Beijing Institute of Technology · TravelSky Technology Limited · School of Computer Science, University of Auckland
Understanding the Behaviors of Environment-aware Information Retrieval
Jun 15, 2026Ruifeng Yuan, Chaohao Yuan, David Dai +4Retrieval-Augmented Generation Reinforcement Learning
1Fudan University · 2Alibaba DAMO Academy · 3Chinese University of Hong Kong +2
RL-Index: Reinforcement Learning for Retrieval Index Reasoning
Jun 15, 2026Yongjia Lei, Nedim Lipka, Zhisheng Qi +5Reinforcement Learning Agentic Retrieval
♡University of Oregon · ♣Adobe Research
Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses
Jun 1, 2026Pengcheng Jiang, Zhiyi Shi, Kelly Hong +5Reinforcement Learning Agentic Retrieval
University of Illinois at Urbana-Champaign · †UC Berkeley · ✧Chroma
RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents
May 25, 2026Mingchen Li, Hansi Zeng, Zhuo Qian +3RL for Language Model Reasoning Credit Assignment in RL
University of Massachusetts, Amherst · Texas Tech University · University of Connecticut
CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search
Apr 19, 2026Hansi Zeng, Liam Collins, Bhuvesh Kumar +2Reinforcement Learning Learning to Rank
Center for Intelligent Information Retrieval, University of Massachusetts Amherst · Snap Inc.
Document Optimization for Black-Box Retrieval via Reinforcement Learning
Apr 6, 2026Omri Uzan, Ron Polonsky, Douwe Kiela +1RL for IR Large Language Model-Guided Optimization
Stanford University · ContextualAI

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Beyond Outcome Rewards: Constructing and Assigning Retrieval Credit for Search Agents

Wenyu Huang, Xinyu Hou, Pavlos Vougiouklis, Ruofei Lai+1

University of Edinburgh, UK · Huawei Technologies Research & Development (UK) Limited

Reward Design for RL RL for IR Step-Level Credit Assignment in RL Reinforcement Learning with Verifiable Rewards

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Search agents enable Large Language Models (LLMs) to iteratively retrieve and use information for complex multi-hop questions. Reinforcement Learning with Verifiable Rewards (RLVR) offers a promising approach for post-training such agents, but its reliance on sparse, outcome-based supervision can make credit assignment difficult and limit learning efficiency. In this paper, we systematically investigate how intermediate supervision can improve reinforcement learning for search agents. We study a range of reward-shaping and credit-assignment strategies that provide learning signals from intermediate retrieval steps. Building on these insights, we develop a training framework that combines intermediate signals with final outcome rewards to improve learning from multi-step search trajectories. Experiments across multiple benchmarks under matched training conditions demonstrate improvements in aggregate search-agent performance and show that both the choice of intermediate signal and where its credit is assigned affect training behaviour. These findings show that reward design and credit assignment are important design dimensions for training effective search agents.

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