“Rewarding Chatbots for Real-World Engagement With Millions of Users”, Irvine Et Al 2023

“Rewarding Chatbots for Real-World Engagement with Millions of Users”⁠, Robert Irvine, Douglas Boubert, Vyas Raina, Adian Liusie, Vineet Mudupalli, Aliaksei Korshuk, Zongyi Liu2023-03-10 (GPT; similar)

“Use GPT-3 Incorrectly: Reduce Costs 40× and Increase Speed by 5×”, Pullen2023

“Use GPT-3 incorrectly: reduce costs 40× and increase speed by 5×”⁠, Alistar Pullen2023-02-06 (knowledge distillation⁠, GPT; backlinks; similar)

“Big Tech Was Moving Cautiously on AI. Then Came ChatGPT. Google, Facebook and Microsoft Helped Build the Scaffolding of AI. Smaller Companies Are Taking It to the Masses, Forcing Big Tech to React.”, Tiku Et Al 2023

“Big Tech was moving cautiously on AI. Then came ChatGPT. Google, Facebook and Microsoft helped build the scaffolding of AI. Smaller companies are taking it to the masses, forcing Big Tech to react.”⁠, Nitasha Tiku, Gerrit De Vynck, Will Oremus2023-01-27 (GPT; similar)

“The inside Story of ChatGPT: How OpenAI Founder Sam Altman Built the World’s Hottest Technology With Billions from Microsoft”, Kahn2023

“The inside story of ChatGPT: How OpenAI founder Sam Altman built the world’s hottest technology with billions from Microsoft”⁠, Jeremy Kahn2023-01-25 (GPT⁠, AI scaling/economics; backlinks; similar)

“Mysteries of Mode Collapse § Inescapable Wedding Parties”, Janus2022

“Mysteries of mode collapse § Inescapable wedding parties”⁠, Janus2022-11-08 (AI safety; similar; bibliography)

“CARP: Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning”, Castricato Et Al 2022

“CARP: Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning”⁠, Louis Castricato, Alexander Havrilla, Shahbul, Matiana, Michael Pieler, Anbang Ye, Ian Yang, Spencer Frazier2022-10-14 (ML dataset⁠, GPT fiction; similar; bibliography)

“Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”, Ganguli Et Al 2022

“Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”⁠, Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez2022-08-25 (ML dataset⁠, NN sampling⁠, GPT⁠, AI safety⁠, RL scaling; similar; bibliography)

“Basis for Intentions (BASIS): Efficient Inverse Reinforcement Learning Using Past Experience”, Abdulhai Et Al 2022

“Basis for Intentions (BASIS): Efficient Inverse Reinforcement Learning using Past Experience”⁠, Marwa Abdulhai, Natasha Jaques, Sergey Levine2022-08-09 (similar)

“Improved Policy Optimization for Online Imitation Learning”, Lavington Et Al 2022

“Improved Policy Optimization for Online Imitation Learning”⁠, Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt2022-07-29 (model-free RL; similar)

“Quark: Controllable Text Generation With Reinforced Unlearning”, Lu Et Al 2022

“Quark: Controllable Text Generation with Reinforced Unlearning”⁠, Ximing Lu, Sean Welleck, Liwei Jiang, Jack Hessel, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, Yejin Choi2022-05-26 (GPT⁠, Decision Transformer; similar)

“RL With KL Penalties Is Better Viewed As Bayesian Inference”, Korbak Et Al 2022

“RL with KL penalties is better viewed as Bayesian inference”⁠, Tomasz Korbak, Ethan Perez, Christopher L. Buckley2022-05-23 (Bayes; similar)

“Housekeep: Tidying Virtual Households Using Commonsense Reasoning”, Kant Et Al 2022

“Housekeep: Tidying Virtual Households using Commonsense Reasoning”⁠, Yash Kant, Arun Ramachandran, Sriram Yenamandra, Igor Gilitschenski, Dhruv Batra, Andrew Szot, Harsh Agrawal2022-05-22 (ML dataset⁠, Transformer NN⁠, model-based RL⁠, robotics; backlinks; similar)

“Imitating, Fast and Slow: Robust Learning from Demonstrations via Decision-time Planning”, Qi Et Al 2022

“Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning”⁠, Carl Qi, Pieter Abbeel, Aditya Grover2022-04-07 (GAN⁠, video analysis; similar)

“Inferring Rewards from Language in Context”, Lin Et Al 2022

“Inferring Rewards from Language in Context”⁠, Jessy Lin, Daniel Fried, Dan Klein, Anca Dragan2022-04-05 (similar)

“SURF: Semi-supervised Reward Learning With Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning”, Park Et Al 2022

“SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning”⁠, Jongjin Park, Younggyo Seo, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee2022-03-18 (robotics; similar)

“InstructGPT: Training Language Models to Follow Instructions With Human Feedback”, Ouyang Et Al 2022

“InstructGPT: Training language models to follow instructions with human feedback”⁠, Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal2022-03-04 (GPT⁠, RL scaling; similar)

“Safe Deep RL in 3D Environments Using Human Feedback”, Rahtz Et Al 2022

“Safe Deep RL in 3D Environments using Human Feedback”⁠, Matthew Rahtz, Vikrant Varma, Ramana Kumar, Zachary Kenton, Shane Legg, Jan Leike2022-01-20 (RL exploration⁠, AI safety; similar)

“A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models”, Zhang Et Al 2022

“A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models”⁠, Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, Dawei Song2022-01-14 (NN sampling⁠, GPT; similar)

“WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing”, Hilton Et Al 2021

“WebGPT: Improving the factual accuracy of language models through web browsing”⁠, Jacob Hilton, Suchir Balaji, Reiichiro Nakano, John Schulman2021-12-16 (retrieval AI⁠, Codex⁠, inner monologue (AI)⁠, RL scaling; similar; bibliography)

“WebGPT: Browser-assisted Question-answering With Human Feedback”, Nakano Et Al 2021

“WebGPT: Browser-assisted question-answering with human feedback”⁠, Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu Long Ouyang, Christina Kim, Christopher Hesse2021-12-16 (ML dataset⁠, retrieval AI⁠, Codex⁠, RL scaling; similar; bibliography)

“Modeling Strong and Human-Like Gameplay With KL-Regularized Search”, Jacob Et Al 2021

“Modeling Strong and Human-Like Gameplay with KL-Regularized Search”⁠, Athul Paul Jacob, David J. Wu, Gabriele Farina, Adam Lerer, Anton Bakhtin, Jacob Andreas, Noam Brown2021-12-14 (AI chess⁠, Diplomacy AI⁠, MARL; similar)

“A General Language Assistant As a Laboratory for Alignment”, Askell Et Al 2021

“A General Language Assistant as a Laboratory for Alignment”⁠, Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph2021-12-01 (GPT⁠, emergence (AI)⁠, meta-learning⁠, AI safety; similar; bibliography)

“Cut the CARP: Fishing for Zero-shot Story Evaluation”, Shahbul Et Al 2021

“Cut the CARP: Fishing for zero-shot story evaluation”⁠, Shahbul, Matiana, J. R. Smith, Ryan Teehan, Louis Castricato, Stella Biderman, Leo Gao, Spencer Frazier2021-10-06 (GPT fiction; similar)

“Recursively Summarizing Books With Human Feedback”, Wu Et Al 2021

“Recursively Summarizing Books with Human Feedback”⁠, Jeff Wu, Long Ouyang, Daniel M. Ziegler, Nisan Stiennon, Ryan Lowe, Jan Leike, Paul Christiano2021-09-22 (compressed Transformer⁠, RL scaling; similar; bibliography)

“B-Pref: Benchmarking Preference-Based Reinforcement Learning”, Lee Et Al 2021

“B-Pref: Benchmarking Preference-Based Reinforcement Learning”⁠, Kimin Lee, Laura Smith, Anca Dragan, Pieter Abbeel2021-06-08 (active learning; similar)

“Trajectory Transformer: Reinforcement Learning As One Big Sequence Modeling Problem”, Janner Et Al 2021

“Trajectory Transformer: Reinforcement Learning as One Big Sequence Modeling Problem”⁠, Michael Janner, Qiyang Colin Li, Sergey Levine2021-06-03 (RL exploration⁠, Decision Transformer; backlinks; similar; bibliography)

“A Survey of Preference-Based Reinforcement Learning Methods”, Wirth Et Al 2021

“A Survey of Preference-Based Reinforcement Learning Methods”⁠, Christian Wirth, Riad Akrour, Gerhard Neumann, Johannes Fürnkranz2021-05-20 (similar)

“Learning What To Do by Simulating the Past”, Lindner Et Al 2021

“Learning What To Do by Simulating the Past”⁠, David Lindner, Rohin Shah, Pieter Abbeel, Anca Dragan2021-04-08 (similar)

“Language Models Have a Moral Dimension”, Schramowski Et Al 2021

“Language Models have a Moral Dimension”⁠, Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin Rothkopf, Kristian Kersting2021-03-08 (GPT⁠, ethics⁠, AI safety; backlinks; similar)

“Brain-computer Interface for Generating Personally Attractive Images”, Spape Et Al 2021

“Brain-computer interface for generating personally attractive images”⁠, Michiel Spape, Keith M. Davis III, Lauri Kangassalo, Niklas Ravaja, Zania Sovijarvi-Spape, Tuukka Ruotsalo2021-02-12 (StyleGAN⁠, RL/brain-imitation-learning; similar)

“Process for Adapting Language Models to Society (PALMS) With Values-Targeted Datasets”, Solaiman & Dennison2021

“Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets”⁠, Irene Solaiman, Christy Dennison2021 (GPT⁠, AI scaling; similar)

“Human-centric Dialog Training via Offline Reinforcement Learning”, Jaques Et Al 2020

“Human-centric Dialog Training via Offline Reinforcement Learning”⁠, Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Shane Gu2020-10-12 (RNN⁠, model-free RL; similar)

“Learning to Summarize from Human Feedback”, Stiennon Et Al 2020

“Learning to summarize from human feedback”⁠, Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei2020-09-02 (GPT⁠, RL scaling; similar)

“Learning Personalized Models of Human Behavior in Chess”, McIlroy-Young Et Al 2020

“Learning Personalized Models of Human Behavior in Chess”⁠, Reid McIlroy-Young, Russell Wang, Siddhartha Sen, Jon Kleinberg, Ashton Anderson2020-08-23 (chess psychology⁠, AI chess⁠, AlphaGo⁠, stylometry; similar)

“Aligning Superhuman AI With Human Behavior: Chess As a Model System”, McIlroy-Young Et Al 2020

“Aligning Superhuman AI with Human Behavior: Chess as a Model System”⁠, Reid McIlroy-Young, Siddhartha Sen, Jon Kleinberg, Ashton Anderson2020-06-02 (chess psychology⁠, AI chess⁠, AlphaGo; similar)

“Active Preference-Based Gaussian Process Regression for Reward Learning”, Bıyık Et Al 2020

“Active Preference-Based Gaussian Process Regression for Reward Learning”⁠, Erdem Bıyık, Nicolas Huynh, Mykel J. Kochenderfer, Dorsa Sadigh2020-05-06 (robotics; similar)

“Bayesian REX: Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences”, Brown Et Al 2020

“Bayesian REX: Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences”⁠, Daniel S. Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum2020-02-21 (Bayes; backlinks; similar)

“RL Agents Implicitly Learning Human Preferences”, Wichers2020

“RL agents Implicitly Learning Human Preferences”⁠, Nevan Wichers2020-02-14 (autoencoder NN; similar)

“Reward-rational (implicit) Choice: A Unifying Formalism for Reward Learning”, Jeon Et Al 2020

“Reward-rational (implicit) choice: A unifying formalism for reward learning”⁠, Hong Jun Jeon, Smitha Milli, Anca D. Dragan2020-02-12 (AI safety; similar)

“What Does BERT Dream Of? A Visual Investigation of Nightmares in Sesame Street”, Bäuerle & Wexler2020

“What does BERT dream of? A visual investigation of nightmares in Sesame Street”⁠, Alex Bäuerle, James Wexler2020-01-13 (GPT; backlinks; similar)

“Deep Bayesian Reward Learning from Preferences”, Brown & Niekum2019

“Deep Bayesian Reward Learning from Preferences”⁠, Daniel S. Brown, Scott Niekum2019-12-10 (similar)

“Learning Norms from Stories: A Prior for Value Aligned Agents”, Frazier Et Al 2019

“Learning Norms from Stories: A Prior for Value Aligned Agents”⁠, Spencer Frazier, Md Sultan Al Nahian, Mark Riedl, Brent Harrison2019-12-07 (GPT fiction⁠, ethics⁠, AI safety; backlinks; similar)

“Learning Human Objectives by Evaluating Hypothetical Behavior”, Reddy Et Al 2019

“Learning Human Objectives by Evaluating Hypothetical Behavior”⁠, Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike2019-12-05 (RL exploration; similar)

“Reinforcement Learning Upside Down: Don’t Predict Rewards—Just Map Them to Actions”, Schmidhuber2019

“Reinforcement Learning Upside Down: Don’t Predict Rewards—Just Map Them to Actions”⁠, Juergen Schmidhuber2019-12-05 (Decision Transformer⁠, robotics; similar)

“Preference-Based Learning for Exoskeleton Gait Optimization”, Tucker Et Al 2019

“Preference-Based Learning for Exoskeleton Gait Optimization”⁠, Maegan Tucker, Ellen Novoseller, Claudia Kann, Yanan Sui, Yisong Yue, Joel Burdick, Aaron D. Ames2019-09-26 (similar)

“Do Massively Pretrained Language Models Make Better Storytellers?”, See Et Al 2019

“Do Massively Pretrained Language Models Make Better Storytellers?”⁠, Abigail See, Aneesh Pappu, Rohun Saxena, Akhila Yerukola, Christopher D. Manning2019-09-24 (GPT fiction; backlinks; similar)

“Fine-Tuning GPT-2 from Human Preferences § Bugs Can Optimize for Bad Behavior”, Ziegler Et Al 2019

“Fine-Tuning GPT-2 from Human Preferences § Bugs can optimize for bad behavior”⁠, Daniel Ziegler, Nisan Stiennon, Jeffrey Wu, Tom Brown, Dario Amodei, Alec Radford, Paul Christiano, Geoffrey Irving2019-09-19 (GPT⁠, AI safety; similar)

“Fine-Tuning GPT-2 from Human Preferences”, Ziegler Et Al 2019

“Fine-Tuning GPT-2 from Human Preferences”⁠, Daniel Ziegler, Nisan Stiennon, Jeffrey Wu, Tom Brown, Dario Amodei, Alec Radford, Paul Christiano, Geoffrey Irving2019-09-19 (GPT; backlinks; similar)

“Fine-Tuning Language Models from Human Preferences”, Ziegler Et Al 2019

“Fine-Tuning Language Models from Human Preferences”⁠, Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano2019-09-18 (GPT⁠, RL scaling; similar)

“Lm-human-preferences”, Ziegler Et Al 2019

“lm-human-preferences”⁠, Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano2019-09-14 (GPT; backlinks; similar)

“Better Rewards Yield Better Summaries: Learning to Summarise Without References”, Böhm Et Al 2019

“Better Rewards Yield Better Summaries: Learning to Summarise Without References”⁠, Florian Böhm, Yang Gao, Christian M. Meyer, Ori Shapira, Ido Dagan, Iryna Gurevych2019-09-03 (backlinks; similar)

“Dueling Posterior Sampling for Preference-Based Reinforcement Learning”, Novoseller Et Al 2019

“Dueling Posterior Sampling for Preference-Based Reinforcement Learning”⁠, Ellen R. Novoseller, Yibing Wei, Yanan Sui, Yisong Yue, Joel W. Burdick2019-08-04 (similar)

“Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog”, Jaques Et Al 2019

“Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog”⁠, Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu2019-06-30 (similar)

“Reward Learning from Human Preferences and Demonstrations in Atari”, Ibarz Et Al 2018

“Reward learning from human preferences and demonstrations in Atari”⁠, Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei2018-11-15 (similar)

“StreetNet: Preference Learning With Convolutional Neural Network on Urban Crime Perception”, Fu Et Al 2018

“StreetNet: Preference Learning with Convolutional Neural Network on Urban Crime Perception”⁠, Kaiqun Fu, Zhiqian Chen, Chang-Tien Lu2018-11-01 (CNN⁠, crime; backlinks; similar; bibliography)

“Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making”, Zintgraf Et Al 2018

“Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making”⁠, Luisa M. Zintgraf, Diederik M. Roijers, Sjoerd Linders, Catholijn M. Jonker, Ann Nowé2018-02-21 (decision theory; similar)

“Convergence of Value Aggregation for Imitation Learning”, Cheng & Boots2018

“Convergence of Value Aggregation for Imitation Learning”⁠, Ching-An Cheng, Byron Boots2018-01-22 (similar)

“A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents”, Wu & Lin2017

“A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents”⁠, Yueh-Hua Wu, Shou-De Lin2017-12-12 (similar)

“Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces”, Warnell Et Al 2017

“Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces”⁠, Garrett Warnell, Nicholas Waytowich, Vernon Lawhern, Peter Stone2017-09-28 (backlinks; similar)

“DropoutDAgger: A Bayesian Approach to Safe Imitation Learning”, Menda Et Al 2017

“DropoutDAgger: A Bayesian Approach to Safe Imitation Learning”⁠, Kunal Menda, Katherine Driggs-Campbell, Mykel J. Kochenderfer2017-09-18 (Bayes; similar)

“Towards Personalized Human AI Interaction—adapting the Behavior of AI Agents Using Neural Signatures of Subjective Interest”, Shih Et Al 2017

“Towards personalized human AI interaction—adapting the behavior of AI agents using neural signatures of subjective interest”⁠, Victor Shih, David C. Jangraw, Paul Sajda, Sameer Saproo2017-09-14 (RL/brain-imitation-learning; similar)

“Learning Human Behaviors from Motion Capture by Adversarial Imitation”, Merel Et Al 2017

“Learning human behaviors from motion capture by adversarial imitation”⁠, Josh Merel, Yuval Tassa, Dhruva TB, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, Nicolas Heess2017-07-07 (similar)

“Learning from Human Preferences”, Amodei Et Al 2017

“Learning from Human Preferences”⁠, Dario Amodei, Paul Christiano, Alex Ray2017-06-13 (backlinks; similar)

“Learning through Human Feedback”, Leike Et Al 2017

“Learning through human feedback”⁠, Jan Leike, Miljan Martic, Shane Legg2017-06-12 (backlinks; similar)

“Deep Reinforcement Learning from Human Preferences”, Christiano Et Al 2017

“Deep reinforcement learning from human preferences”⁠, Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei2017-06-12 (RL scaling⁠, order statistics/comparison; similar)

“An Invitation to Imitation”, Bagnell2015

“An Invitation to Imitation”⁠, J. Andrew Bagnell2015-03-14 (model-free RL⁠, robotics; backlinks; similar)

“Just Sort It! A Simple and Effective Approach to Active Preference Learning”, Maystre & Grossglauser2015

“Just Sort It! A Simple and Effective Approach to Active Preference Learning”⁠, Lucas Maystre, Matthias Grossglauser2015-02-19 (active learning⁠, order statistics/comparison; backlinks; similar)

“Bayesian Active Learning for Classification and Preference Learning”, Houlsby Et Al 2011

“Bayesian Active Learning for Classification and Preference Learning”⁠, Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, Máté Lengyel2011-12-24 (active learning⁠, Bayes⁠, order statistics/comparison; backlinks; similar)

“DAGGER: A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning”, Ross Et Al 2010

“DAGGER: A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning”⁠, Stephane Ross, Geoffrey J. Gordon, J. Andrew Bagnell2010-11-02 (model-free RL; backlinks; similar)

“Transformers As Variational Autoencoders”

“Transformer-VAE for Program Synthesis”

“Learning through Human Feedback [blog]”

“Learning through human feedback [blog]”