RL for RAG

RAG: Retrieval-Augmented Generation · RL: Reinforcement Learning

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Latest papers

20 papers5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

TRIO: Task Refinement with Individual Optimization for Retrieval-Augmented Generation
Nov 2, 2026Yingtao Ren, Xiao Luo, Yiwei Fu +3Retrieval-Augmented Generation Iterative RAG
University of Technology Sydney · University of Wisconsin–Madison · Peking University
Two Birds with One Stone: Skill-Augmented Generation via Unified Retrieval and Utilization
Oct 28, 2026Zihan Luo, Jiahui Kang, Wei Zhou +3Knowledge Augmentation for Language Models RL for RAG
Chongqing University, Chongqing, China · Huazhong University of Science and Technology, Wuhan, China
CalibraRL: Confidence-Calibrated Reinforcement Learning for Mitigating Hallucinations in RAG
Oct 26, 2026Qianchi Zhang, Hainan Zhang, Liang Pang +2Hallucination in RAG RL for RAG
School of Artificial Intelligence, Beihang University, Beijing, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · Beijing Academy of Blockchain and Edge Computing, Beijing, China
Self-Assessment Reinforcement Learning for Search-State Awareness in Retrieval-Augmented Large Language Models
Oct 21, 2026Taewhoo Lee, Minju Song, Jungwoo Park +6State Tracking in Language Models RL for RAG
Korea University · Amazon · AIGEN Sciences +2
Learning to Program Retrievals: Composing Search Primitives via Reinforcement Learning
Oct 17, 2026Kyuyoung Kim, Zilin Xiao, Zhiyuan Wang +4Retrieval-Augmented Reasoning RL for RAG
KAIST AI · Meta Superintelligence Labs
Copying Is Not Grounding: Training Faithful Long-Form RAG with Authored-Sentence Credit
Oct 13, 2026Hoang Pham, Duc Anh Vu, Duy Tung Doan +2RL for RAG RAG Citation Faithfulness
Nanyang Technological University · Viettel AI, Viettel Group · VinUniversity
BRIDGE: Bilevel Retrieval-Credit-Aware Agentic Reinforcement Learning
Sep 29, 2026Quan Xiao, Mingda Liu, Gaowen Liu +2Reinforcement Learning Agentic RAG
Department of Electrical and Computer Engineering, Cornell University · Institute of Integrated Research, Institute of Science Tokyo · Cisco Research
Compression Beyond the Uncompressed: A Two-Stage Training Recipe for Soft Context Compression in RAG
Sep 4, 2026Shuyu Guo, Shuo Zhang, Zhaochun RenRL for RAG LLM Inference Acceleration
Shandong University Qingdao, China · Bloomberg London, United Kingdom · Leiden University Leiden, The Netherlands
PRO-Step: Step-level Process Reward Optimization for Retrieval-Augmented Generation
Aug 31, 2026MinKeon Kim, Namjun Lee, Jaekwang KimRetrieval-Augmented Generation Retrieval-Augmented Reasoning
Department of Applied Artificial Intelligence, Convergence Program for Social Innovation, Sungkyunkwan University, Seoul, South Korea
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 +7RL for LLM Agents Credit Assignment in RL
National University of Defense Technology · Changsha, China
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
CRITIC-R1: Learning Structured Critics for Retrieval-Augmented Generation
May 28, 2026Wenhan Xiao, Ziwei Zhang, Chuanyue Yu +4LLM Self-Correction Retrieval-Augmented Generation
Nankai University · Beihang University · Guangxi Normal University
Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL
May 14, 2026Jianbo Zhu, Xing Fang, Jing Wang +5Semantic ID Recommendation E-Commerce Search
Taobao & Tmall Group of Alibaba Hangzhou, China
CroSearch-R1: Better Leveraging Cross-lingual Knowledge for Retrieval-Augmented Generation
Apr 28, 2026Rui Qi, Fengran Mo, Sijin Lu +3Multilingual RAG Retrieval-Augmented Generation
School of Computer Science and Technology, Beijing Jiaotong University Beijing, China · RALI, Université de Montréal Montréal, Québec, Canada
AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning
Apr 19, 2026Jingbo Sun, Wenyue Chong, Songjun Tu +7Agentic Retrieval Agentic RAG
SKL-MAIS, Institute of Automation, Chinese Academy of Sciences · School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences +1
OThink-SRR1: Search, Refine and Reasoning with Reinforced Learning for Large Language Models
Mar 27, 2026Haijian Liang, Zenghao Niu, Junjie Wu +3RL for RAG Agentic RAG
Shenzhen University · OPPO Research Institute
APEX-Searcher: Refining Credit Assignment with Subgoaling for Agentic Retrieval-Augmented Generation
Mar 14, 2026Kun Chen, Qingchao Kong, Zhao Feifei +1Agentic RAG RL for RAG
University of Chinese Academy of Sciences · MAIS, Institute of Automation, Chinese Academy of Sciences · Wenge Technology Co., Ltd
Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training
Feb 26, 2026Tianle Xia, Ming Xu, Lingxiang Hu +7RL for RAG Agentic RAG
Tencent
HyperGraphPro: Progress-Aware Reinforcement Learning for Structure-Guided Hypergraph RAG
Jan 25, 2026Jinyoung Park, Sanghyeok Lee, Omar Zia Khan +2Hypergraph-Based RAG Multi-Hop Retrieval
KAIST · Microsoft · Amazon

Nov 2, 2026J/K move · Enter open · S save

TRIO: Task Refinement with Individual Optimization for Retrieval-Augmented Generation

Yingtao Ren, Xiao Luo, Yiwei Fu, Jinzhao Zhou+2

University of Technology Sydney · University of Wisconsin–Madison · Peking University

Retrieval-Augmented Generation Iterative RAG Agentic RAG RL for RAG

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This paper studies retrieval-augmented generation (RAG), which leverages external knowledge to improve the performance of language models. Despite the remarkable progress, current RAG approaches are still far from satisfactory due to inadequate retrieval and potential hallucinations. Towards this end, we propose a novel approach named Task Refinement with Individual Optimization (TRIO) for RAG. The core idea of our TRIO is to equip three well-designed agents with an iterative refinement paradigm by aligning their individual optimization objectives. Specifically, we first expand ambiguous queries with a query extension agent and quantify the reward based on the improved answer quality, which can be utilized to refine our agent for higher expansion efficiency. To enhance the retrieval quality, we include a unanimity voting to annotate the expanded query as insufficient or sufficient, and their generative outcomes are utilized as ground truth to optimize the judge agent. To further improve evidence utilization and answer accuracy, an answer agent is optimized with dynamic matching-based rewards with curriculum learning for final outputs. Extensive experiments across multiple benchmark datasets validate the effectiveness of the proposed TRIO in comparison with state-of-the-art approaches. Our code is available at https://github.com/yingtaoren/TRIO.