Noah Y. Siegel

According to our database, Noah Y. Siegel authored at least 25 papers between 2019 and 2026.

Collaborative distances:

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

2026

A Positive Case for Faithfulness: LLM Self-Explanations Help Predict Model Behavior.

Harry Mayne, Justin Singh Kang, Dewi Gould, Kannan Ramchandran, Adam Mahdi, Noah Y. Siegel

CoRR, February, 2026

2025

LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring.

Chloe Li, Mary Phuong, Noah Y. Siegel

CoRR, August, 2025

Advancing Event Forecasting through Massive Training of Large Language Models: Challenges, Solutions, and Broader Impacts.

Sang-Woo Lee, Sohee Yang, Donghyun Kwak, Noah Y. Siegel

CoRR, July, 2025

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, Oana-Maria Camburu

CoRR, March, 2025

LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring.

Chloe Li, Noah Y. Siegel

Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, 2025

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, Arunkumar Byravan, Leonard Hasenclever, Yuval Tassa, Fereshteh Sadeghi, Nathan Batchelor, Federico Casarini, Stefano Saliceti, Charles Game, Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber, Nicole Hurley, Francesco Nori, Raia Hadsell, Nicolas Heess

Dataset, April, 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, Arunkumar Byravan, Leonard Hasenclever, Yuval Tassa, Fereshteh Sadeghi, Nathan Batchelor, Federico Casarini, Stefano Saliceti, Charles Game, Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber, Nicole Hurley, Francesco Nori, Raia Hadsell, Nicolas Heess

Sci. Robotics, 2024

The Effect of Model Size on LLM Post-hoc Explainability via LIME.

Henning Heyen, Amy Widdicombe, Noah Y. Siegel, María Pérez-Ortiz, Philip C. Treleaven

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

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

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2024

2022

Figure Data for the paper "From Motor Control to Team Play in Simulated Humanoid Football".

Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, Nicolas Heess

Dataset, August, 2022

From motor control to team play in simulated humanoid football.

Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, Nicolas Heess

Sci. Robotics, 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

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, Ben Moran, Noah Y. Siegel, Andrea Huber, Francesco Romano, Nathan Batchelor, Federico Casarini, Josh Merel, Raia Hadsell, Nicolas Heess

CoRR, 2022

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

Proceedings of the 38th International Conference on Machine Learning, 2021

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, Martin A. Riedmiller, S. M. Ali Eslami, Nicolas Heess

Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021

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

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

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

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

Proceedings of the Robotics: Science and Systems XVI, 2020

Critic Regularized Regression.

Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh Merel, Jost Tobias Springenberg, Scott E. Reed, Bobak Shahriari, Noah Y. Siegel, Çaglar Gülçehre, Nicolas Heess, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Proceedings of the 8th International Conference on Learning Representations, 2020

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

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 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

Proceedings of the 3rd Annual Conference on Robot Learning, 2019