Fetching the paper…
Reading the bibliography…
Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal technique for large language model (LLM) alignment.
Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
Earlier work this paper cites.
The k-armed dueling bandits problem
Yisong Yue, Josef Broder, Robert Kleinberg, and Thorsten Joachims · 2012
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord · 2018
Earlier work this paper cites.
Battle of bandits
Aadirupa Saha and Aditya Gopalan · 2018
Earlier work this paper cites.
On the weaknesses of reinforcement learning for neural machine translation
Leshem Choshen, Lior Fox, Zohar Aizenbud, and Omri Abend · 2019
Earlier work this paper cites.
Adversarial nli: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela · 2019
Earlier work this paper cites.
Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2019
Earlier work this paper cites.
Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
Earlier work this paper cites.
Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
Earlier work this paper cites.
Implementation matters in deep policy gradients: A case study on ppo and trpo
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry · 2020
Earlier work this paper cites.
Improved optimistic algorithms for logistic bandits
Louis Faury, Marc Abeille, Clément Calauzènes, and Olivier Fercoq · 2020
Earlier work this paper cites.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
Earlier work this paper cites.
Dueling posterior sampling for preference-based reinforcement learning
Ellen Novoseller, Yibing Wei, Yanan Sui, Yisong Yue, and Joel Burdick · 2020
Earlier work this paper cites.
Preference-based reinforcement learning with finite-time guarantees
Yichong Xu, Ruosong Wang, Lin Yang, Aarti Singh, and Artur Dubrawski · 2020
Earlier work this paper cites.
A general language assistant as a laboratory for alignment. arxiv
A Askell, Y Bai, A Chen, D Drain, D Ganguli, T Henighan, A Jones, N Joseph, B Mann, N DasSarma, et al · 2021
Earlier work this paper cites.
Preference-based online learning with dueling bandits: A survey
Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, and Eyke Hüllermeier · 2021
Earlier work this paper cites.
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, et al · 2021
Earlier work this paper cites.
Dueling rl: reinforcement learning with trajectory preferences
Aldo Pacchiano, Aadirupa Saha, and Jonathan Lee · 2021
Earlier work this paper cites.
Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approximation
Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, and Liwei Wang · 2022
Earlier work this paper cites.
TruthfulQA: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Gec: A unified framework for interactive decision making in mdp, pomdp, and beyond
Han Zhong, Wei Xiong, Sirui Zheng, Liwei Wang, Zhaoran Wang, Zhuoran Yang, and Tong Zhang · 2022
Cited alongside, same era.
Gpqa: A graduate-level google-proof q&a benchmark
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman · 2023
Cited alongside, same era.
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
Later among the works it cites.
Alpacafarm: A simulation framework for methods that learn from human feedback
Yann Dubois, Chen Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy S Liang, and Tatsunori B Hashimoto · 2024
Later among the works it cites.
Efficient exploration for llms
Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao, and Benjamin Van Roy · 2024
Later among the works it cites.
Regressing the relative future: Efficient policy optimization for multi-turn rlhf
Zhaolin Gao, Wenhao Zhan, Jonathan D Chang, Gokul Swamy, Kianté Brantley, Jason D Lee, and Wen Sun · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Prasann Singhal, Tanya Goyal, Jiacheng Xu, and Greg Durrett · 2023
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Is rlhf more difficult than standard rl?
Yuanhao Wang, Qinghua Liu, and Chi Jin · 2023
Cited alongside, same era.
Making rl with preference-based feedback efficient via randomization
Runzhe Wu and Wen Sun · 2023
Cited alongside, same era.
Some things are more cringe than others: Preference optimization with the pairwise cringe loss
Jing Xu, Andrew Lee, Sainbayar Sukhbaatar, and Jason Weston · 2023
Cited alongside, same era.
How to query human feedback efficiently in rl?
Wenhao Zhan, Masatoshi Uehara, Wen Sun, and Jason D Lee · 2023
Cited alongside, same era.
Provable offline reinforcement learning with human feedback
Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D Lee, and Wen Sun · 2023
Cited alongside, same era.
Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Rame, Thomas Mesnard, Yao Zhao, Bilal Piot, et al · 2024
Later among the works it cites.
Self-play with adversarial critic: Provable and scalable offline alignment for language models
Xiang Ji, Sanjeev Kulkarni, Mengdi Wang, and Tengyang Xie · 2024
Later among the works it cites.
Mitigating the alignment tax of rlhf
Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, et al · 2024
Later among the works it cites.
Provably mitigating overoptimization in rlhf: Your sft loss is implicitly an adversarial regularizer
Zhihan Liu, Miao Lu, Shenao Zhang, Boyi Liu, Hongyi Guo, Yingxiang Yang, Jose Blanchet, and Zhaoran Wang · 2024
Later among the works it cites.
Simpo: Simple preference optimization with a reference-free reward
Yu Meng, Mengzhou Xia, and Danqi Chen · 2024
Later among the works it cites.
Language model alignment with elastic reset
Michael Noukhovitch, Samuel Lavoie, Florian Strub, and Aaron C Courville · 2024
Later among the works it cites.
Introducing chatgpt, 2022
OpenAI · 2024
Later among the works it cites.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
Later among the works it cites.
Direct nash optimization: Teaching language models to self-improve with general preferences
Corby Rosset, Ching-An Cheng, Arindam Mitra, Michael Santacroce, Ahmed Awadallah, and Tengyang Xie · 2024
Later among the works it cites.
Multi-turn reinforcement learning from preference human feedback
Lior Shani, Aviv Rosenberg, Asaf Cassel, Oran Lang, Daniele Calandriello, Avital Zipori, Hila Noga, Orgad Keller, Bilal Piot, Idan Szpektor, et al · 2024
Later among the works it cites.
The crucial role of samplers in online direct preference optimization
Ruizhe Shi, Runlong Zhou, and Simon S Du · 2024
Later among the works it cites.
Self-play preference optimization for language model alignment
Yue Wu, Zhiqing Sun, Huizhuo Yuan, Kaixuan Ji, Yiming Yang, and Quanquan Gu · 2024
Later among the works it cites.
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
Later among the works it cites.
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
Later among the works it cites.
Self-exploring language models: Active preference elicitation for online alignment
Shenao Zhang, Donghan Yu, Hiteshi Sharma, Han Zhong, Zhihan Liu, Ziyi Yang, Shuohang Wang, Hany Hassan, and Zhaoran Wang · 2024
Later among the works it cites.
Dpo meets ppo: Reinforced token optimization for rlhf
Han Zhong, Guhao Feng, Wei Xiong, Xinle Cheng, Li Zhao, Di He, Jiang Bian, and Liwei Wang · 2024
Later among the works it cites.