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)

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)

Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning. - Tianyu Li; Nathan O. Lambert; Roberto Calandra; Franziska Meier; Akshara Rai (ICRA, 2020)

AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks. - McKane Andrus; Sarah Dean; Thomas Krendl Gilbert; Nathan Lambert 001; Tom Zick (ISTAS, 2020)

Objective Mismatch in Model-based Reinforcement Learning. - Nathan O. Lambert; Brandon Amos; Omry Yadan; Roberto Calandra (L4DC, 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)

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 (AISTATS, 2021)

Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning. - Nathan O. Lambert; Albert Wilcox; Howard Zhang; Kristofer S. J. Pister; Roberto Calandra (CDC, 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 (MRS, 2021)

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning. - Luis Pineda; Brandon Amos; Amy Zhang 001; Nathan O. Lambert; Roberto Calandra (CoRR, 2021)

Axes for Sociotechnical Inquiry in AI Research. - Sarah Dean; Thomas Krendl Gilbert; Nathan Lambert 001; Tom Zick (CoRR, 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)

The Challenges of Exploration for Offline Reinforcement Learning. - Nathan Lambert 001; Markus Wulfmeier; William F. Whitney; Arunkumar Byravan; Michael Bloesch; Vibhavari Dasagi; Tim Hertweck; Martin A. Riedmiller (CoRR, 2022)

Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems. - Thomas Krendl Gilbert; Sarah Dean; Tom Zick; Nathan Lambert 001 (CoRR, 2022)

Investigating Compounding Prediction Errors in Learned Dynamics Models. - Nathan O. Lambert; Kristofer S. J. Pister; Roberto Calandra (CoRR, 2022)

Reward Reports for Reinforcement Learning. - Thomas Krendl Gilbert; Sarah Dean; Nathan Lambert 001; Tom Zick; Aaron J. Snoswell (CoRR, 2022)

Measuring Data. - Margaret Mitchell; Alexandra Sasha Luccioni; Nathan Lambert 001; 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; Nathan Lambert 001; Sarah Dean; Tom Zick; Aaron J. Snoswell; Soham Mehta (AIES, 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)

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; et al. (CoRR, 2023)

Entangled Preferences: The History and Risks of Reinforcement Learning and Human Feedback. - Nathan Lambert 001; Thomas Krendl Gilbert; Tom Zick (CoRR, 2023)

Zephyr: Direct Distillation of LM Alignment. - Lewis Tunstall; Edward Beeching; Nathan Lambert 001; Nazneen Rajani; Kashif Rasul; Younes Belkada; Shengyi Huang; Leandro von Werra; Clémentine Fourrier; Nathan Habib; Nathan Sarrazin; Omar Sanseviero; et al. (CoRR, 2023)

The Alignment Ceiling: Objective Mismatch in Reinforcement Learning from Human Feedback. - Nathan O. Lambert; Roberto Calandra (CoRR, 2023)

Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2. - Hamish Ivison; Yizhong Wang; Valentina Pyatkin; Nathan Lambert 001; Matthew E. Peters; Pradeep Dasigi; Joel Jang; David Wadden; Noah A. Smith; Iz Beltagy; Hannaneh Hajishirzi (CoRR, 2023)

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; et al. (ACL (1), 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; et al. (ACL (1), 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 001; Milan Mossé; Eric Pacuit; Stuart Russell 001; Hailey Schoelkopf; Emanuel Tewolde; William S. Zwicker (ICML, 2024)

WildGuard: Open One-stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs. - Seungju Han 002; Kavel Rao; Allyson Ettinger; Liwei Jiang; Bill Yuchen Lin; Nathan Lambert 001; Yejin Choi 001; Nouha Dziri (NeurIPS, 2024)

Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback. - Hamish Ivison; Yizhong Wang; Jiacheng Liu 010; Zeqiu Wu; Valentina Pyatkin; Nathan Lambert 001; Noah A. Smith; Yejin Choi 001; Hanna Hajishirzi (NeurIPS, 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; et al. (CoRR, 2024)

RewardBench: Evaluating Reward Models for Language Modeling. - Nathan Lambert 001; Valentina Pyatkin; Jacob Morrison; LJ Miranda; Bill Yuchen Lin; Khyathi Raghavi Chandu; Nouha Dziri; Sachin Kumar 009; Tom Zick; Yejin Choi 001; Noah A. Smith; Hannaneh Hajishirzi (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 001; Milan Mossé; Eric Pacuit; Stuart Russell 001; Hailey Schoelkopf; Emanuel Tewolde; William S. Zwicker (CoRR, 2024)

D2PO: Discriminator-Guided DPO with Response Evaluation Models. - Prasann Singhal; Nathan Lambert 001; Scott Niekum; Tanya Goyal; Greg Durrett (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 001; et al. (CoRR, 2024)

Self-Directed Synthetic Dialogues and Revisions Technical Report. - Nathan Lambert 001; Hailey Schoelkopf; Aaron Gokaslan; Luca Soldaini; Valentina Pyatkin; Louis Castricato (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 001; Yuling Gu; Shane Arora; et al. (CoRR, 2024)

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models. - Matt Deitke; Christopher Clark; Sangho Lee 008; Rohun Tripathi; Yue Yang 006; Jae Sung Park; Mohammadreza Salehi; Niklas Muennighoff; Kyle Lo; Luca Soldaini; Jiasen Lu; Taira Anderson; et al. (CoRR, 2024)

TÜLU 3: Pushing Frontiers in Open Language Model Post-Training. - Nathan Lambert 001; Jacob Morrison; Valentina Pyatkin; Shengyi Huang; Hamish Ivison; Faeze Brahman; Lester James V. Miranda; Alisa Liu; Nouha Dziri; Shane Lyu; Yuling Gu; Saumya Malik; et al. (CoRR, 2024)

A Survey on Data Selection for Language Models. - Alon Albalak; Yanai Elazar; Sang Michael Xie; Shayne Longpre; Nathan Lambert 001; Xinyi Wang 003; Niklas Muennighoff; Bairu Hou; Liangming Pan; Haewon Jeong; Colin Raffel; Shiyu Chang; et al. (Trans. Mach. Learn. Res., 2024)

A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning. - Ran Wei; Nathan Lambert 001; Anthony D. McDonald; Alfredo García 001; Roberto Calandra (Trans. Mach. Learn. Res., 2024)

M-RewardBench: Evaluating Reward Models in Multilingual Settings. - Srishti Gureja; Lester James Validad Miranda; Shayekh Bin Islam; Rishabh Maheshwary; Drishti Sharma; Gusti Triandi Winata; Nathan Lambert 001; Sebastian Ruder; Sara Hooker; Marzieh Fadaee (ACL (1), 2025)

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models. - Matt Deitke; Christopher Clark; Sangho Lee 001; Rohun Tripathi; Yue Yang 006; Jae Sung Park; Mohammadreza Salehi; Niklas Muennighoff; Kyle Lo; Luca Soldaini; Jiasen Lu; Taira Anderson; et al. (CVPR, 2025)

OLMoE: Open Mixture-of-Experts Language Models. - Niklas Muennighoff; Luca Soldaini; Dirk Groeneveld; Kyle Lo; Jacob Morrison; Sewon Min; Weijia Shi; Evan Pete Walsh; Oyvind Tafjord; Nathan Lambert 001; Yuling Gu; Shane Arora; et al. (ICLR, 2025)

RewardBench: Evaluating Reward Models for Language Modeling. - Nathan Lambert 001; Valentina Pyatkin; Jacob Morrison; Lester James V. Miranda; Bill Yuchen Lin; Khyathi Raghavi Chandu; Nouha Dziri; Sachin Kumar 009; Tom Zick; Yejin Choi 001; Noah A. Smith; Hannaneh Hajishirzi (NAACL (Findings), 2025)

2 OLMo 2 Furious. - Team OLMo; Pete Walsh; Luca Soldaini; Dirk Groeneveld; Kyle Lo; Shane Arora; Akshita Bhagia; Yuling Gu; Shengyi Huang; Matt Jordan; Nathan Lambert 001; Dustin Schwenk; et al. (CoRR, 2025)

Reinforcement Learning from Human Feedback. - Nathan Lambert 001 (CoRR, 2025)

RewardBench 2: Advancing Reward Model Evaluation. - Saumya Malik; Valentina Pyatkin; Sander Land; Jacob Morrison; Noah A. Smith; Hannaneh Hajishirzi; Nathan Lambert 001 (CoRR, 2025)

Spurious Rewards: Rethinking Training Signals in RLVR. - Rulin Shao; Shuyue Stella Li; Rui Xin; Scott Geng; Yiping Wang; Sewoong Oh; Simon Shaolei Du; Nathan Lambert 001; Sewon Min; Ranjay Krishna; Yulia Tsvetkov; Hannaneh Hajishirzi; et al. (CoRR, 2025)

Generalizing Verifiable Instruction Following. - Valentina Pyatkin; Saumya Malik; Victoria Graf; Hamish Ivison; Shengyi Huang; Pradeep Dasigi; Nathan Lambert 001; Hannaneh Hajishirzi (CoRR, 2025)