Superalignment Fast Grants

* We are offering $100K–$2M grants for academic labs, nonprofits, and individual researchers.

* For graduate students, we are sponsoring a one-year $150K OpenAI Superalignment Fellowship: $75K in stipend and $75K in compute and research funding.

* No prior experience working on alignment is required; we are actively looking to support researchers who are excited to work on alignment for the first time.

* Our application process is simple, and we’ll get back to you within four weeks of applications closing.

* Apply by February 18(opens in a new window)

With these grants, we are particularly interested in funding the following research directions⁠(opens in a new window):

* Weak-to-strong generalization: Humans will be weak supervisors relative to superhuman models. Can we understand and control how strong models generalize from weak supervision⁠?

* Interpretability: How can we understand model internals? And can we use this to e.g. build an AI lie detector?

* Scalable oversight: How can we use AI systems to assist humans in evaluating the outputs of other AI systems on complex tasks?

* Many other research directions, including but not limited to: honesty, chain-of-thought faithfulness, adversarial robustness, evals and testbeds, and more.

For more on the research directions, FAQs, and other details, see our Superalignment Fast Grants page⁠(opens in a new window).

Join us in this challenge

We think new researchers could make enormous contributions! This is a young field with many tractable research problems; outstanding contributions could not just help shape the field, but be critical for the future of AI. There has never been a better time to start working on alignment.

* Alignment

* 2023

Author

OpenAI

Contributors

Leopold Aschenbrenner, Jan Leike, Sherry Lachman, Aleksander Madry, Chris Clark, Collin Burns, Pavel Izmailov, Nat McAleese, William Saunders, Bobby Wu, Lisa Pan, Janine Korovesis, Ilya Sutskever, Elie Georges, Kayla Wood, Kendra Rimbach, Thomas Degry, Ruby Chen

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