AGENTIC R AG-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing
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AI 研究摘要
The paper presents AGENTIC R AG-R1, a reinforcement-learning framework for retrieval-augmented multi-step reasoning. It combines fine-grained actions, stack memory with push, pop, revision, and summarization, hierarchical outcome and process rewards, and information-aware rollout rejection. Experiments report comparisons, ablations, step-budget scaling, model-size effects, timing, and downstream agent evaluations.
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这意味着什么
The design may help agents revise misleading intermediate retrievals instead of accumulating them during long reasoning chains. This could benefit question-answering and research agents that need adaptive retrieval and memory control. Its value remains conditional on substantial training, judging, retrieval, checkpoint, and evaluator infrastructure that is not fixed in this compilation.
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研究结论
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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.实验受阻，详见具体原因报告 33.55 个百分点
报告
33.55 个百分点
观测
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No verified repository, trained checkpoint, complete curated training data, retrieval implementation, or strict evaluator is available in the fixed inputs.
average F1 · 尚未评估
报告: 33.55 个百分点
F1 · 尚未评估
报告: 44 个百分点
F1 · 尚未评估
报告: 49.21 个百分点
The paper reports increasing AGENTIC R AG-R1 F1 as the maximum reasoning-step budget grows on selected 2Wiki, Bamboogle, and TriviaQA evaluations.实验受阻，详见具体原因报告 23.91 个百分点
报告
23.91 个百分点
观测
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The missing checkpoint, retrieval service, and complete evaluation protocol prevent strict comparable evidence; execution resources are not the blocker.
F1 · 尚未评估
报告: 23.91 个百分点
F1 · 尚未评估
报告: 47.45 个百分点
The paper reports lower average inference time than TC-RAG and downstream results on MedQA, DeepResearch Bench, ALFWorld, and WebShop.实验受阻，详见具体原因报告 10.15 seconds
报告
10.15 seconds
观测
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The original hardware-sensitive benchmark conditions are underspecified by the fixed paper and repository, so a strict comparable verdict is not available.
average inference time · 尚未评估
报告: 10.15 seconds
average inference time · 尚未评估
报告: 11.86 seconds
answer accuracy · 尚未评估
报告: 87 个百分点
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.实验受阻，详见具体原因报告 33.55 个百分点
报告
33.55 个百分点
观测
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The trained variants, reward model prompts, curated training set, optimizer implementation, and exact seeds are unavailable.
average F1 · 尚未评估
报告: 33.55 个百分点
average F1 · 尚未评估
报告: 19.94 个百分点
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