AGENTIC R AG-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing · CiteArk

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AGENTIC R AG-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing

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Authors:Xinke Jiang, Yue Fang, Zhibang Yang, Jiaran Gao, Zhixin Zhang, Tao Feng, Rihong Qiu, Wentao Zhang, Hongxin Ding, Ruizhe Zhang, Yongxin Xu, Yuheng Huang, Xu Chu, Junfeng Zhao, Yasha Wang

arXiv 2026 Artificial Intelligence Machine Learning (cs.LG)Multiagent Systems Machine Learning (stat.ML)

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AGENTIC R AG-R1 reports higher or near-highest F1 than listed baselines across seven multi-hop, open-domain, and agentic QA benchmarks.

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The paper reports increasing AGENTIC R AG-R1 F1 as the maximum reasoning-step budget grows on selected 2Wiki, Bamboogle, and TriviaQA evaluations.

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The paper reports lower average inference time than TC-RAG and downstream results on MedQA, DeepResearch Bench, ALFWorld, and WebShop.

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The paper reports that removing memory actions, rollout rejection, or both lowers average F1, and that removing both retrieval and memory rewards gives the lowest ablation average.

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