Imagined Value Gradients: Model-Based Policy Optimization with Tranferable Latent Dynamics Models. - Arunkumar Byravan; Jost Tobias Springenberg; Abbas Abdolmaleki; Roland Hafner; Michael Neunert; Thomas Lampe; Noah Y. Siegel; Nicolas Heess; Martin A. Riedmiller (CoRL, 2019)

Regularized Hierarchical Policies for Compositional Transfer in Robotics. - Markus Wulfmeier; Abbas Abdolmaleki; Roland Hafner; Jost Tobias Springenberg; Michael Neunert; Tim Hertweck; Thomas Lampe; Noah Y. Siegel; Nicolas Heess; Martin A. Riedmiller (CoRR, 2019)

Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models. - Arunkumar Byravan; Jost Tobias Springenberg; Abbas Abdolmaleki; Roland Hafner; Michael Neunert; Thomas Lampe; Noah Y. Siegel; Nicolas Heess; Martin A. Riedmiller (CoRR, 2019)

Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning. - Noah Y. Siegel; Jost Tobias Springenberg; Felix Berkenkamp; Abbas Abdolmaleki; Michael Neunert; Thomas Lampe; Roland Hafner; Nicolas Heess; Martin A. Riedmiller (ICLR, 2020)

Critic Regularized Regression. - Ziyu Wang 001; Alexander Novikov 001; Konrad Zolna; Josh Merel; Jost Tobias Springenberg; Scott E. Reed; Bobak Shahriari; Noah Y. Siegel; Çaglar Gülçehre; Nicolas Heess; Nando de Freitas (NeurIPS, 2020)

Compositional Transfer in Hierarchical Reinforcement Learning. - Markus Wulfmeier; Abbas Abdolmaleki; Roland Hafner; Jost Tobias Springenberg; Michael Neunert; Noah Y. Siegel; Tim Hertweck; Thomas Lampe; Nicolas Heess; Martin A. Riedmiller (Robotics: Science and Systems, 2020)

Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning. - Noah Y. Siegel; Jost Tobias Springenberg; Felix Berkenkamp; Abbas Abdolmaleki; Michael Neunert; Thomas Lampe; Roland Hafner; Nicolas Heess; Martin A. Riedmiller (CoRR, 2020)

Simple Sensor Intentions for Exploration. - Tim Hertweck; Martin A. Riedmiller; Michael Bloesch; Jost Tobias Springenberg; Noah Y. Siegel; Markus Wulfmeier; Roland Hafner; Nicolas Heess (CoRR, 2020)

"What, not how": Solving an under-actuated insertion task from scratch. - Giulia Vezzani; Michael Neunert; Markus Wulfmeier; Rae Jeong; Thomas Lampe; Noah Y. Siegel; Roland Hafner; Abbas Abdolmaleki; Martin A. Riedmiller; Francesco Nori (CoRR, 2020)

Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion. - Michael Bloesch; Jan Humplik; Viorica Patraucean; Roland Hafner; Tuomas Haarnoja; Arunkumar Byravan; Noah Yamamoto Siegel; Saran Tunyasuvunakool; Federico Casarini; Nathan Batchelor; Francesco Romano; Stefano Saliceti; et al. (CoRL, 2021)

Data-efficient Hindsight Off-policy Option Learning. - Markus Wulfmeier; Dushyant Rao; Roland Hafner; Thomas Lampe; Abbas Abdolmaleki; Tim Hertweck; Michael Neunert; Dhruva Tirumala; Noah Y. Siegel; Nicolas Heess; Martin A. Riedmiller (ICML, 2021)

Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors. - Steven Bohez; Saran Tunyasuvunakool; Philemon Brakel; Fereshteh Sadeghi; Leonard Hasenclever; Yuval Tassa; Emilio Parisotto; Jan Humplik; Tuomas Haarnoja; Roland Hafner; Markus Wulfmeier; Michael Neunert; et al. (CoRR, 2022)

Solving math word problems with process- and outcome-based feedback. - Jonathan Uesato; Nate Kushman; Ramana Kumar; H. Francis Song; Noah Y. Siegel; Lisa Wang; Antonia Creswell; Geoffrey Irving; Irina Higgins (CoRR, 2022)

From motor control to team play in simulated humanoid football. - Siqi Liu 002; Guy Lever; Zhe Wang 055; Josh Merel; S. M. Ali Eslami; Daniel Hennes; Wojciech M. Czarnecki; Yuval Tassa; Shayegan Omidshafiei; Abbas Abdolmaleki; Noah Y. Siegel; Leonard Hasenclever; et al. (Sci. Robotics, 2022)

Figure Data for the paper "From Motor Control to Team Play in Simulated Humanoid Football". - Siqi Liu 002; Guy Lever; Zhe Wang 055; Josh Merel; S. M. Ali Eslami; Daniel Hennes; Wojciech Czarnecki 001; Yuval Tassa; Shayegan Omidshafiei; Abbas Abdolmaleki; Noah Y. Siegel; Leonard Hasenclever; et al. (Zenodo, 2022)

The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Models. - Noah Y. Siegel; Oana-Maria Camburu; Nicolas Heess; María Pérez-Ortiz 001 (ACL (Short Papers), 2024)

On scalable oversight with weak LLMs judging strong LLMs. - Zachary Kenton; Noah Y. Siegel; János Kramár; Jonah Brown-Cohen; Samuel Albanie; Jannis Bulian; Rishabh Agarwal; David Lindner; Yunhao Tang; Noah D. Goodman; Rohin Shah (NeurIPS, 2024)

The Effect of Model Size on LLM Post-hoc Explainability via LIME. - Henning Heyen; Amy Widdicombe; Noah Y. Siegel; María Pérez-Ortiz 001; Philip C. Treleaven (CoRR, 2024)

Learning agile soccer skills for a bipedal robot with deep reinforcement learning. - Tuomas Haarnoja; Ben Moran; Guy Lever; Sandy H. Huang; Dhruva Tirumala; Jan Humplik; Markus Wulfmeier; Saran Tunyasuvunakool; Noah Y. Siegel; Roland Hafner; Michael Bloesch; Kristian Hartikainen; et al. (Sci. Robotics, 2024)

Data Release for: Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning. - Tuomas Haarnoja; Ben Moran; Guy Lever; Sandy H. Huang; Dhruva Tirumala; Jan Humplik; Markus Wulfmeier; Saran Tunyasuvunakool; Noah Y. Siegel; Roland Hafner; Michael Bloesch; Kristian Hartikainen; et al. (Zenodo, 2024)

Faithfulness of LLM Self-Explanations for Commonsense Tasks: Larger Is Better, and Instruction-Tuning Allows Trade-Offs but Not Pareto Dominance. - Noah Y. Siegel; Nicolas Heess; María Pérez-Ortiz 001; Oana-Maria Camburu (CoRR, 2025)

Advancing Event Forecasting through Massive Training of Large Language Models: Challenges, Solutions, and Broader Impacts. - Sang-Woo Lee 001; Sohee Yang; Donghyun Kwak; Noah Y. Siegel (CoRR, 2025)

LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring. - Chloe Li; Mary Phuong; Noah Y. Siegel (CoRR, 2025)