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We propose Soft Preference Optimization (SPO), a method for aligning generative models, such as Large Language Models (LLMs), with human preferences, without the need for a reward model.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry · 1952
Earlier work this paper cites.
The analysis of permutations
Robin L Plackett · 1975
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Yoav Freund and Robert E Schapire · 1997
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Individual choice behavior: A theoretical analysis
R Duncan Luce · 2005
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
A general theoretical paradigm to understand learning from human preferences
Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, and Rémi Munos · 2023
Earlier work this paper cites.
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Earlier work this paper cites.
Alpacafarm: A simulation framework for methods that learn from human feedback, 2023
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https://huggingface.co/karpathy/tinyllamas/commit/5c22d0a5f31b635f3e4bf8f2d4dd87363ae3a275 , 2023
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Direct preference optimization with an offset
Afra Amini, Tim Vieira, and Ryan Cotterell · 2024
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On softmax direct preference optimization for recommendation
Yuxin Chen, Junfei Tan, An Zhang, Zhengyi Yang, Leheng Sheng, Enzhi Zhang, Xiang Wang, and Tat-Seng Chua · 2024
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Provably robust dpo: Aligning language models with noisy feedback
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Kto: Model alignment as prospect theoretic optimization
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llama2.c: Inference llama 2 in one file of pure c
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Statistical rejection sampling improves preference optimization
Tianqi Liu, Yao Zhao, Rishabh Joshi, Misha Khalman, Mohammad Saleh, Peter J Liu, and Jialu Liu · 2023
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Nash learning from human feedback
Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Andrea Michi, et al · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Zephyr: Direct distillation of lm alignment
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, et al · 2023
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Some things are more cringe than others: Preference optimization with the pairwise cringe loss
Jing Xu, Andrew Lee, Sainbayar Sukhbaatar, and Jason Weston · 2023
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The wisdom of hindsight makes language models better instruction followers
Tianjun Zhang, Fangchen Liu, Justin Wong, Pieter Abbeel, and Joseph E Gonzalez · 2023
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Yu Meng, Mengzhou Xia, and Danqi Chen · 2024
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Disentangling length from quality in direct preference optimization
Ryan Park, Rafael Rafailov, Stefano Ermon, and Chelsea Finn · 2024
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From r to q*: Your language model is secretly a q-function
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Direct nash optimization: Teaching language models to self-improve with general preferences
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A minimaximalist approach to reinforcement learning from human feedback
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Rrhf: Rank responses to align language models with human feedback
Hongyi Yuan, Zheng Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, and Fei Huang · 2024
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