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We propose to scale RL to unverifiable data with a novel algorithm JEPO (Jensen's Evidence lower bound Policy Optimization).
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Sympy: symbolic computing in python
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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Reinforcement learning and control as probabilistic inference: Tutorial and review
Sergey Levine · 2018
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Tighter variational bounds are not necessarily better
Tom Rainforth, Adam Kosiorek, Tuan Anh Le, Chris Maddison, Maximilian Igl, Frank Wood, and Yee Whye Teh · 2018
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Buy 4 reinforce samples, get a baseline for free!
Wouter Kool, Herke van Hoof, and Max Welling · 2019
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Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Rewriting history with inverse rl: Hindsight inference for policy improvement
Ben Eysenbach, Xinyang Geng, Sergey Levine, and Russ R Salakhutdinov · 2020
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Direct language model alignment from online ai feedback
Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Rame, Thomas Mesnard, Yao Zhao, Bilal Piot, et al · 2024
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Training chain-of-thought via latent-variable inference
Matthew Douglas Hoffman, Du Phan, David Dohan, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, and Rif A Saurous · 2024
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Unveiling the statistical foundations of chain-of-thought prompting methods
Xinyang Hu, Fengzhuo Zhang, Siyu Chen, and Zhuoran Yang · 2024
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