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In question-answering (QA) systems, Retrieval-Augmented Generation (RAG) has become pivotal in enhancing response accuracy and reducing hallucination issues.
Language models are few-shot learners
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Towards reasoning in large language models: A survey
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Self-rag: Learning to retrieve, generate, and critique through self-reflection
Asai, A.; Wu, Z.; Wang, Y.; Sil, A.; and Hajishirzi, H. 2023 · 2023
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Learning to Retrieve, Generate, and Critique through Self-Reflection. arXiv 2023
Asai, A.; Wu, Z.; Wang, Y.; Sil, A.; and Hajishirzi, H. S.-R. ???? · 2023
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Gong, P.; and Mao, J. 2023 · 2023
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Reasoning with language model is planning with world model
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Query rewriting for retrieval-augmented large language models
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Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy
Shao, Z.; Gong, Y.; Shen, Y.; Huang, M.; Duan, N.; and Chen, W. 2023 · 2023
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Zhang, E.; Wang, X.; Gong, P.; Lin, Y.; and Mao, J. 2024 · 2024
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Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning
Chen, Y.; Yan, L.; Sun, W.; Ma, X.; Zhang, Y.; Wang, S.; Yin, D.; Yang, Y.; and Mao, J. 2025 · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Guo, D.; Yang, D.; Zhang, H.; Song, J.; Zhang, R.; Xu, R.; Zhu, Q.; Ma, S.; Wang, P.; Bi, X.; et al. 2025 · 2025
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Search-r1: Training llms to reason and leverage search engines with reinforcement learning
Jin, B.; Zeng, H.; Yue, Z.; Yoon, J.; Arik, S.; Wang, D.; Zamani, H.; and Han, J. 2025 · 2025
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Search-o1: Agentic search-enhanced large reasoning models
Li, X.; Dong, G.; Jin, J.; Zhang, Y.; Zhou, Y.; Zhu, Y.; Zhang, P.; and Dou, Z. 2025 · 2025
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R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
Song, H.; Jiang, J.; Min, Y.; Chen, J.; Chen, Z.; Zhao, W. X.; Fang, L.; and Wen, J.-R. 2025 · 2025
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Yang, A.; Li, A.; Yang, B.; Zhang, B.; Hui, B.; Zheng, B.; Yu, B.; Gao, C.; Huang, C.; Lv, C.; et al. 2025 · 2025
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