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Reinforcement learning from human feedback (RLHF) has emerged as the primary method for aligning large language models (LLMs) with human preferences.
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Scikit-learn: Machine learning in Python
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Leveraging sparse linear layers for debuggable deep networks
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al · 2022
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Understanding dataset difficulty with 𝒱 \mathcal{V} -usable information
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta · 2022
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Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned, 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Defining and characterizing reward hacking. corr, abs/2209.13085, 2022. doi: 10.48550
Joar Skalse, Nikolaus HR Howe, Dmitrii Krasheninnikov, and David Krueger · 2022
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Rest meets react: Self-improvement for multi-step reasoning llm agent
Renat Aksitov, Sobhan Miryoosefi, Zonglin Li, Daliang Li, Sheila Babayan, Kavya Kopparapu, Zachary Fisher, Ruiqi Guo, Sushant Prakash, Pranesh Srinivasan, et al · 2023
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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
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Reward model ensembles help mitigate overoptimization
Thomas Coste, Usman Anwar, Robert Kirk, and David Krueger · 2023
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Ultrafeedback: Boosting language models with high-quality feedback, 2023
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Amplify-instruct: Synthetically generated diverse multi-turn conversations for efficient llm training
Luigi Daniele and Suphavadeeprasit · 2023
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RAFT: Reward ranked finetuning for generative foundation model alignment
Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang, Winnie Chow, Rui Pan, Shizhe Diao, Jipeng Zhang, KaShun SHUM, and Tong Zhang · 2023
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Scaling laws for reward model overoptimization
Leo Gao, John Schulman, and Jacob Hilton · 2023
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Reinforced self-training (rest) for language modeling
Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, et al · 2023
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Judging LLM-as-a-judge with MT-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica · 2023
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Odin: Disentangled reward mitigates hacking in rlhf
Lichang Chen, Chen Zhu, Davit Soselia, Jiuhai Chen, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, and Bryan Catanzaro · 2024
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Rlhf workflow: From reward modeling to online rlhf
Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, and Tong Zhang · 2024
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Direct language model alignment from online ai feedback
Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Rame, Thomas Mesnard, Yao Zhao, Bilal Piot, et al · 2024
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Teaching large language models to reason with reinforcement learning
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Beavertails: Towards improved safety alignment of LLM via a human-preference dataset
Jiaming Ji, Mickel Liu, Juntao Dai, Xuehai Pan, Chi Zhang, Ce Bian, Boyuan Chen, Ruiyang Sun, Yizhou Wang, and Yaodong Yang · 2023
Cited alongside, same era.
Llm-blender: Ensembling large language models with pairwise comparison and generative fusion
Dongfu Jiang, Xiang Ren, and Bill Yuchen Lin · 2023
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Remax: A simple, effective, and efficient reinforcement learning method for aligning large language models
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Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe · 2023
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Yong Lin, Lu Tan, Hangyu Lin, Zeming Zheng, Renjie Pi, Jipeng Zhang, Shizhe Diao, Haoxiang Wang, Han Zhao, Yuan Yao, et al · 2023
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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
Cited alongside, same era.
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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Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, Christoforos Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro, Sainbayar Sukhbaatar, and Roberta Raileanu · 2024
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Revisiting scalarization in multi-task learning: A theoretical perspective
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Rewardbench: Evaluating reward models for language modeling
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Introducing meta llama 3: The most capable openly available llm to date
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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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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Mingchuan Zhang, YK Li, Y Wu, and Daya Guo · 2024
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A minimaximalist approach to reinforcement learning from human feedback
Gokul Swamy, Christoph Dann, Rahul Kidambi, Zhiwei Steven Wu, and Alekh Agarwal · 2024
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Generalized preference optimization: A unified approach to offline alignment
Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, and Bilal Piot · 2024
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Martin Weyssow, Aton Kamanda, and Houari Sahraoui · 2024
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Exploratory preference optimization: Harnessing implicit q*-approximation for sample-efficient rlhf
Tengyang Xie, Dylan J Foster, Akshay Krishnamurthy, Corby Rosset, Ahmed Awadallah, and Alexander Rakhlin · 2024
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A theoretical analysis of nash learning from human feedback under general kl-regularized preference
Chenlu Ye, Wei Xiong, Yuheng Zhang, Nan Jiang, and Tong Zhang · 2024
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Yi: Open foundation models by 01. ai
Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, et al · 2024
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Self-exploring language models: Active preference elicitation for online alignment
Shenao Zhang, Donghan Yu, Hiteshi Sharma, Ziyi Yang, Shuohang Wang, Hany Hassan, and Zhaoran Wang · 2024
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Dpo meets ppo: Reinforced token optimization for rlhf
Han Zhong, Guhao Feng, Wei Xiong, Li Zhao, Di He, Jiang Bian, and Liwei Wang · 2024
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