This page shows the most relevant public items for AI Alignment, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Dec 22, 2025 • arXiv • Percy Liang, Thomas K. V., Eleanor Rigby
Aligning multi-agent systems via traditional human feedback is intractable due to the sheer volume and speed of agent-to-agent interactions. We introduce a novel alignment framework utilizing Inver...
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 • May 31, 2023 • arXiv • Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe
Large language models often struggle with multi-step logical reasoning, frequently hallucinating incorrect steps that invalidate the final answer. To improve reasoning capabilities, we compare two ...
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 14, 2023 • arXiv • Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, Ilya Sutskever, Jeff Wu
As AI models become increasingly capable, we will eventually face the challenge of superalignment: how can humans supervise AI systems that are much smarter than them? To study this empirically tod...
Paper • Jun 21, 2025 • arXiv • Thomas K. V., Eleanor Rigby, M. Hasan
Traditional AI alignment techniques like RLHF are insufficient for Agentic AI, as autonomous systems frequently encounter novel edge cases in open-ended environments that were absent from their tra...
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...
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Use weekly for fast-moving updates, monthly for stable trend confirmation, and all-time for evergreen references. This guidance is specific to AI Alignment topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
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