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.
Research Topics
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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.
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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.
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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
Sid Baines, Jonathan Bostock, Maria Angelica Martinez, Andrew Draganov, David Africa, Daniel Tan
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
Sid Baines, Jonathan Bostock, Maria Angelica Martinez, Andrew Draganov, David Africa, Daniel Tan
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
Sid Baines, Jonathan Bostock, Maria Angelica Martinez, Andrew Draganov, David Africa, Daniel Tan
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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Our Team
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.
Mailing list
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.