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To understand the goals and goal representations of AI systems, we carefully study a pretrained reinforcement learning policy that solves mazes by navigating to a range of target squares.
Leveraging Procedural Generation to Benchmark Reinforcement Learning, July 2020
Karl Cobbe, Christopher Hesse, Jacob Hilton, and John Schulman · 1912
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Interpretable machine learning: Fundamental principles and 10 grand challenges
Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, and Chudi Zhong · 1935
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Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart Russell, et al · 2000
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An introduction to statistical learning , volume 112
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, et al · 2013
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Imitation learning: A survey of learning methods
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne · 2017
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Proximal Policy Optimization Algorithms, August 2017
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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A survey on neural network interpretability
Yu Zhang, Peter Tiňo, Aleš Leonardis, and Ke Tang · 2017
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IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, and Koray Kavukcuoglu · 2018
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Explaining Explanations: An Overview of Interpretability of Machine Learning
Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal · 2018
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Scalable agent alignment via reward modeling: a research direction
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg · 2018
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Behavioral Cloning from Observation, May 2018
Faraz Torabi, Garrett Warnell, and Peter Stone · 2018
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A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
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Understanding rl vision
Jacob Hilton, Nick Cammarata, Shan Carter, Gabriel Goh, and Chris Olah · 2020
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A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, et al · 2021
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On interpretability of artificial neural networks: A survey
Feng-Lei Fan, Jinjun Xiong, Mengzhou Li, and Ge Wang · 2021
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2021
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Discovering language model behaviors with model-written evaluations
Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, et al · 2022
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Goal misgeneralization: Why correct specifications aren’t enough for correct goals
Rohin Shah, Vikrant Varma, Ramana Kumar, Mary Phuong, Victoria Krakovna, Jonathan Uesato, and Zac Kenton · 2022
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Extracting Latent Steering Vectors from Pretrained Language Models, May 2022
Nishant Subramani, Nivedita Suresh, and Matthew E. Peters · 2022
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Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt · 2022
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Causal scrubbing: A method for rigorously testing interpretability hypotheses
Lawrence Chan, Adrià Garriga-Alonso, Nicholas Goldowsky-Dill, Ryan Greenblatt, Jenny Nitishinskaya, Ansh Radhakrishnan, Buck Shlegeris, and Nate Thomas · 2022
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Softmax linear units
Nelson Elhage, Tristan Hume, Catherine Olsson, Neel Nanda, Tom Henighan, Scott Johnston, Sheer ElShowk, Nicholas Joseph, Nova DasSarma, Ben Mann, Danny Hernandez, Amanda Askell, Kamal Ndousse, Andy Jones, Dawn Drain, Anna Chen, Yuntao Bai, Deep Ganguli, Liane Lovitt, Zac Hatfield-Dodds, Jackson Kernion, Tom Conerly, Shauna Kravec, Stanislav Fort, Saurav Kadavath, Josh Jacobson, Eli Tran-Johnson, Jared Kaplan, Jack Clark, Tom Brown, Sam McCandlish, Dario Amodei, and Christopher Olah · 2022
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Improving alignment of dialogue agents via targeted human judgements
Amelia Glaese, Nat McAleese, Maja Trebacz, John Aslanides, Vlad Firoiu, Timo Ewalds, Maribeth Rauh, Laura Weidinger, Martin Chadwick, Phoebe Thacker, et al · 2022
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Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al · 2022
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The alignment problem from a deep learning perspective
Richard Ngo · 2022
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Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases, June 2022
Chris Olah · 2022
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Joseph Bloom and Paul Colognese · 2023
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Direct preference optimization: Your language model is secretly a reward model
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Llama 2: Open foundation and fine-tuned chat models
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