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Process Reward Modeling (PRM) is critical for complex reasoning and decision-making tasks where the accuracy of intermediate steps significantly influences the overall outcome.
Individual choice behavior , volume 4
R Duncan Luce · 1959
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The analysis of permutations
Robin L Plackett · 1975
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Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y Ng, Daishi Harada, and Stuart Russell · 1999
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Playing atari with deep reinforcement learning
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A theoretical analysis of deep q-learning
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Training verifiers to solve math word problems
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Measuring mathematical problem solving with the MATH dataset
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Training language models to follow instructions with human feedback
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Solving math word problems with process- and outcome-based feedback
Jonathan Uesato, Nate Kushman, Ramana Kumar, H. Francis Song, Noah Y. Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen · 2023
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RLAIF: scaling reinforcement learning from human feedback with AI feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Lu, Thomas Mesnard, Colton Bishop, Victor Carbune, and Abhinav Rastogi · 2023
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Making language models better reasoners with step-aware verifier
Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, and Weizhu Chen · 2023
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Tree of thoughts: Deliberate problem solving with large language models
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Llama 3 model card
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Improve mathematical reasoning in language models by automated process supervision
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Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold
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Topologies of reasoning: Demystifying chains, trees, and graphs of thoughts
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