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The remarkable performance of models like the OpenAI o1 can be attributed to their ability to emulate human-like long-time thinking during inference.
A diverse corpus for evaluating and developing english math word problem solvers
Shen-Yun Miao, Chao-Chun Liang, and Keh-Yih Su · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Star: Bootstrapping reasoning with reasoning
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Large language models are reasoning teachers
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Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models
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Graph of thoughts: Solving elaborate problems with large language models
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Mehul Damani, Idan Shenfeld, Andi Peng, Andreea Bobu, and Jacob Andreas · 2024
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Improving factuality and reasoning in language models through multiagent debate
Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch · 2024
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Think before you speak: Training language models with pause tokens
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Token-budget-aware llm reasoning
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Training large language models to reason in a continuous latent space, 2024
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David Heineman, Yao Dou, and Wei Xu · 2024
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When can llms actually correct their own mistakes? a critical survey of self-correction of llms
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Args: Alignment as reward-guided search
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Iterative reasoning preference optimization
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Qwq: Reflect deeply on the boundaries of the unknown, November 2024
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Direct preference optimization: Your language model is secretly a reward model
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Alphazero-like tree-search can guide large language model decoding and training
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Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, et al · 2024
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Escape sky-high cost: Early-stopping self-consistency for multi-step reasoning
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Let’s verify step by step
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Provably mitigating overoptimization in rlhf: Your sft loss is implicitly an adversarial regularizer
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