This page shows the most relevant public items for RLHF, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Oct 19, 2022 • arXiv • Leo Gao, John Schulman, Jacob Hilton
When optimizing a policy against a learned reward model, the policy eventually exploits errors in the reward model, leading to a decline in the true underlying objective. This phenomenon, known as ...
Paper • Jun 12, 2017 • arXiv • Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, Dario Amodei
For many complex real-world tasks, defining a mathematical reward function is difficult, leading to misaligned AI behavior when optimized. We explore a method for solving reinforcement learning tas...
Paper • Dec 16, 2021 • arXiv • Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, John Schulman
We introduce a method for fine-tuning language models to interact with a text-based web browser to answer open-ended questions. This model, WebGPT, searches the web, navigates through links, and sy...
Paper • Sep 2, 2020 • arXiv • Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul Christiano
We show that it is possible to significantly improve the quality of text summaries generated by large language models by training them with reinforcement learning from human feedback. We collect a ...
Paper • Sep 22, 2022 • arXiv • Amelia Glaese, Nat McAleese, Maja Trebacz, John Aslanides, Vlad Firoiu, Geoffrey Irving
We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines. We train our model using reinforcement lea...
Paper • Mar 4, 2022 • arXiv • Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, Ryan Lowe
Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not he...
Paper • Feb 4, 2026 • arXiv • Fan Yang, Rui Meng, Trudi Di Qi, Ali Ezzati, Yuxin Wen
Direct Preference Optimization (DPO) and its variants have revolutionized LLM alignment, yet they struggle when the preferred choice requires deep, multi-step reasoning. We introduce KEPO, a framew...
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