Nathan Lambert

According to our database, Nathan Lambert authored at least 40 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning.

Ran Wei, Nathan Lambert, Anthony D. McDonald, Alfredo García, Roberto Calandra

Trans. Mach. Learn. Res., 2024

A Survey on Data Selection for Language Models.

Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, Colin Raffel, Shiyu Chang, Tatsunori Hashimoto, William Yang Wang

Trans. Mach. Learn. Res., 2024

M-RewardBench: Evaluating Reward Models in Multilingual Settings.

Srishti Gureja, Lester James V. Miranda, Shayekh Bin Islam, Rishabh Maheshwary, Drishti Sharma, Gusti Winata, Nathan Lambert, Sebastian Ruder, Sara Hooker, Marzieh Fadaee

CoRR, 2024

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models.

Matt Deitke, Christopher Clark, Sangho Lee, Rohun Tripathi, Yue Yang, Jae Sung Park, Mohammadreza Salehi, Niklas Muennighoff, Kyle Lo, Luca Soldaini, Jiasen Lu, Taira Anderson, Erin Bransom, Kiana Ehsani, Huong Ngo, Yen-Sung Chen, Ajay Patel, Mark Yatskar, Chris Callison-Burch, Andrew Head, Rose Hendrix, Favyen Bastani, Eli VanderBilt, Nathan Lambert, Yvonne Chou, Arnavi Chheda, Jenna Sparks, Sam Skjonsberg, Michael Schmitz, Aaron Sarnat, Byron Bischoff, Pete Walsh, Chris Newell, Piper Wolters, Tanmay Gupta, Kuo-Hao Zeng, Jon Borchardt, Dirk Groeneveld, Jen Dumas, Crystal Nam, Sophie Lebrecht, Caitlin Wittlif, Carissa Schoenick, Oscar Michel, Ranjay Krishna, Luca Weihs, Noah A. Smith, Hannaneh Hajishirzi, Ross B. Girshick, Ali Farhadi, Aniruddha Kembhavi

CoRR, 2024

OLMoE: Open Mixture-of-Experts Language Models.

Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Jacob Morrison, Sewon Min, Weijia Shi, Pete Walsh, Oyvind Tafjord, Nathan Lambert, Yuling Gu, Shane Arora, Akshita Bhagia, Dustin Schwenk, David Wadden, Alexander Wettig, Binyuan Hui, Tim Dettmers, Douwe Kiela, Ali Farhadi, Noah A. Smith, Pang Wei Koh, Amanpreet Singh, Hannaneh Hajishirzi

CoRR, 2024

Self-Directed Synthetic Dialogues and Revisions Technical Report.

Nathan Lambert, Hailey Schoelkopf, Aaron Gokaslan, Luca Soldaini, Valentina Pyatkin, Louis Castricato

CoRR, 2024

WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs.

Seungju Han, Kavel Rao, Allyson Ettinger, Liwei Jiang, Bill Yuchen Lin, Nathan Lambert, Yejin Choi, Nouha Dziri

CoRR, 2024

Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback.

Hamish Ivison, Yizhong Wang, Jiacheng Liu, Zeqiu Wu, Valentina Pyatkin, Nathan Lambert, Noah A. Smith, Yejin Choi, Hannaneh Hajishirzi

CoRR, 2024

Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence.

Adrien Basdevant, Camille François, Victor Storchan, Kevin Bankston, Ayah Bdeir, Brian Behlendorf, Mérouane Debbah, Sayash Kapoor, Yann LeCun, Mark Surman, Helen King-Turvey, Nathan Lambert, Stefano Maffulli, Nik Marda, Govind Shivkumar, Justine Tunney

CoRR, 2024

D2PO: Discriminator-Guided DPO with Response Evaluation Models.

Prasann Singhal, Nathan Lambert, Scott Niekum, Tanya Goyal, Greg Durrett

CoRR, 2024

Social Choice for AI Alignment: Dealing with Diverse Human Feedback.

Vincent Conitzer, Rachel Freedman, Jobst Heitzig, Wesley H. Holliday, Bob M. Jacobs, Nathan Lambert, Milan Mossé, Eric Pacuit, Stuart Russell, Hailey Schoelkopf, Emanuel Tewolde, William S. Zwicker

CoRR, 2024

RewardBench: Evaluating Reward Models for Language Modeling.

Nathan Lambert, Valentina Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Raghavi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi

CoRR, 2024

OLMo: Accelerating the Science of Language Models.

Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, Hannaneh Hajishirzi

CoRR, 2024

Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback.

Vincent Conitzer, Rachel Freedman, Jobst Heitzig, Wesley H. Holliday, Bob M. Jacobs, Nathan Lambert, Milan Mossé, Eric Pacuit, Stuart Russell, Hailey Schoelkopf, Emanuel Tewolde, William S. Zwicker

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research.

Luca Soldaini, Rodney Kinney, Akshita Bhagia, Dustin Schwenk, David Atkinson, Russell Authur, Ben Bogin, Khyathi Raghavi Chandu, Jennifer Dumas, Yanai Elazar, Valentin Hofmann, Ananya Harsh Jha, Sachin Kumar, Li Lucy, Xinxi Lyu, Nathan Lambert, Ian Magnusson, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Abhilasha Ravichander, Kyle Richardson, Zejiang Shen, Emma Strubell, Nishant Subramani, Oyvind Tafjord, Pete Walsh, Luke Zettlemoyer, Noah A. Smith, Hannaneh Hajishirzi, Iz Beltagy, Dirk Groeneveld, Jesse Dodge, Kyle Lo

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

OLMo: Accelerating the Science of Language Models.

Dirk Groeneveld, Iz Beltagy, Evan Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, Hannaneh Hajishirzi

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

2023

Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2.

Hamish Ivison, Yizhong Wang, Valentina Pyatkin, Nathan Lambert, Matthew E. Peters, Pradeep Dasigi, Joel Jang, David Wadden, Noah A. Smith, Iz Beltagy, Hannaneh Hajishirzi

CoRR, 2023

The Alignment Ceiling: Objective Mismatch in Reinforcement Learning from Human Feedback.

Nathan O. Lambert, Roberto Calandra

CoRR, 2023

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, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, Thomas Wolf

CoRR, 2023

Entangled Preferences: The History and Risks of Reinforcement Learning and Human Feedback.

Nathan Lambert, Thomas Krendl Gilbert, Tom Zick

CoRR, 2023

Confidence-Building Measures for Artificial Intelligence: Workshop Proceedings.

Sarah Shoker, Andrew W. Reddie, Sarah Barrington, Ruby Booth, Miles Brundage, Husanjot Chahal, Michael Depp, Bill Drexel, Ritwik Gupta, Marina Favaro, Jake Hecla, Alan Hickey, Margarita Konaev, Kirthi Kumar, Nathan Lambert, Andrew Lohn, Cullen O'Keefe, Nazneen Rajani, Michael Sellitto, Robert Trager, Leah Walker, Alexa Wehsener, Jessica Young

CoRR, 2023

BLISS: Interplanetary Exploration with Swarms of Low-Cost Spacecraft.

Alexander N. Alvara, Lydia Lee, Emmanuel Sin, Nathan O. Lambert, Andrew J. Westphal, Kristofer S. J. Pister

CoRR, 2023

Reward Reports for Reinforcement Learning.

Thomas Krendl Gilbert, Nathan Lambert, Sarah Dean, Tom Zick, Aaron J. Snoswell, Soham Mehta

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

2022

Measuring Data.

Margaret Mitchell, Alexandra Sasha Luccioni, Nathan Lambert, Marissa Gerchick, Angelina McMillan-Major, Ezinwanne Ozoani, Nazneen Rajani, Tristan Thrush, Yacine Jernite, Douwe Kiela

CoRR, 2022

Reward Reports for Reinforcement Learning.

Thomas Krendl Gilbert, Sarah Dean, Nathan Lambert, Tom Zick, Aaron J. Snoswell

CoRR, 2022

Investigating Compounding Prediction Errors in Learned Dynamics Models.

Nathan O. Lambert, Kristofer S. J. Pister, Roberto Calandra

CoRR, 2022

Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems.

Thomas Krendl Gilbert, Sarah Dean, Tom Zick, Nathan Lambert

CoRR, 2022

The Challenges of Exploration for Offline Reinforcement Learning.

Nathan Lambert, Markus Wulfmeier, William F. Whitney, Arunkumar Byravan, Michael Bloesch, Vibhavari Dasagi, Tim Hertweck, Martin A. Riedmiller

CoRR, 2022

2021

Nonholonomic Yaw Control of an Underactuated Flying Robot With Model-Based Reinforcement Learning.

Nathan O. Lambert, Craig B. Schindler, Daniel S. Drew, Kristofer S. J. Pister

IEEE Robotics Autom. Lett., 2021

Axes for Sociotechnical Inquiry in AI Research.

Sarah Dean, Thomas Krendl Gilbert, Nathan Lambert, Tom Zick

CoRR, 2021

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning.

Luis Pineda, Brandon Amos, Amy Zhang, Nathan O. Lambert, Roberto Calandra

CoRR, 2021

BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-Agent and Swarm Control.

Mark Selden, Jason Zhou, Felipe Campos, Nathan O. Lambert, Daniel S. Drew, Kristofer S. J. Pister

Proceedings of the International Symposium on Multi-Robot and Multi-Agent Systems, 2021

Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning.

Nathan O. Lambert, Albert Wilcox, Howard Zhang, Kristofer S. J. Pister, Roberto Calandra

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning.

Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan O. Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Learning for Microrobot Exploration: Model-based Locomotion, Sparse-robust Navigation, and Low-power Deep Classification.

Nathan O. Lambert, Farhan Toddywala, Brian Liao, Eric Zhu, Lydia Lee, Kristofer S. J. Pister

CoRR, 2020

Objective Mismatch in Model-based Reinforcement Learning.

Nathan O. Lambert, Brandon Amos, Omry Yadan, Roberto Calandra

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks.

McKane Andrus, Sarah Dean, Thomas Krendl Gilbert, Nathan Lambert, Tom Zick

Proceedings of the IEEE International Symposium on Technology and Society, 2020

Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning.

Tianyu Li, Nathan O. Lambert, Roberto Calandra, Franziska Meier, Akshara Rai

Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

2019

Low-Level Control of a Quadrotor With Deep Model-Based Reinforcement Learning.

Nathan O. Lambert, Daniel S. Drew, Joseph Yaconelli, Sergey Levine, Roberto Calandra, Kristofer S. J. Pister

IEEE Robotics Autom. Lett., 2019

2018

Toward Controlled Flight of the Ionocraft: A Flying Microrobot Using Electrohydrodynamic Thrust With Onboard Sensing and No Moving Parts.

Daniel S. Drew, Nathan O. Lambert, Craig B. Schindler, Kristofer S. J. Pister

IEEE Robotics Autom. Lett., 2018