Research

Alignment Research

The Arcadia Alignment team is a group of alignment researchers based in London. We study the alignment techniques that frontier labs will take bets on, and evaluate them publicly.


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Research Topics

Model Motivations

The effects of large-scale reinforcement learning on what models value and intend are little understood. We investigate how training shapes model motivations, and how it can erode alignment.

Model Motivations

The effects of large-scale reinforcement learning on what models value and intend are little understood. We investigate how training shapes model motivations, and how it can erode alignment.

Model Motivations

The effects of large-scale reinforcement learning on what models value and intend are little understood. We investigate how training shapes model motivations, and how it can erode alignment.

Scalable Oversight

We study how to apply scalable oversight techniques towards aligning models we might not be able to control.

Scalable Oversight

We study how to apply scalable oversight techniques towards aligning models we might not be able to control.

Scalable Oversight

We study how to apply scalable oversight techniques towards aligning models we might not be able to control.

Automated Alignment

We study where automated alignment research stands today, where it fails, and how it can help future alignment work without speeding up capabilities.

Automated Alignment

We study where automated alignment research stands today, where it fails, and how it can help future alignment work without speeding up capabilities.

Automated Alignment

We study where automated alignment research stands today, where it fails, and how it can help future alignment work without speeding up capabilities.

Our research

Stress-Testing Alignment Midtraining

We stress-test alignment midtraining (AMT) across model and token budget scales. Our results suggest that midtraining cannot tackle the hard problems of AI alignment—namely distributional shift and reward underspecification in the presence of imperfect data.

Stress-Testing Alignment Midtraining

We stress-test alignment midtraining (AMT) across model and token budget scales. Our results suggest that midtraining cannot tackle the hard problems of AI alignment—namely distributional shift and reward underspecification in the presence of imperfect data.

Stress-Testing Alignment Midtraining

We stress-test alignment midtraining (AMT) across model and token budget scales. Our results suggest that midtraining cannot tackle the hard problems of AI alignment—namely distributional shift and reward underspecification in the presence of imperfect data.

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Future opportunities

The alignment team is not currently hiring. Click here to see our other open roles, or sign up to our mailing list to hear when new positions open.

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Stay updated on our work.

Questions?

Feel free to reach out to us via our contact form.

Mailing list

Stay updated on our work.

Questions?

Feel free to reach out to us via our contact form.