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Large reasoning models (LRMs) excel at solving complex tasks by leveraging long chain-of-thought (CoT) reasoning.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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System 1+ system 2= better world: Neural-symbolic chain of logic reasoning
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Efficient memory management for large language model serving with pagedattention
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Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023 · 2023
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Do not think that much for 2+ 3=? on the overthinking of o1-like llms
Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, and 1 others. 2024 · 2024
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Let’s verify step by step
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
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From system 1 to system 2: A survey of reasoning large language models
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O1-pruner: Length-harmonizing fine-tuning for o1-like reasoning pruning
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Reasoning models can be effective without thinking
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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