4 sources
One of the earlier Anthropic contributors worth tracking if you care about the transition from RLHF-style assistant training into scaling and evaluation work.
Researcher Profile
Editor reviewedSam McCandlish
Alignment via AI feedback (Constitutional AI)
Chief Architect at Anthropic
One of the clearest people to follow if you care about scaling laws, training efficiency, and the systems choices that quietly shape frontier-model progress.
Organizations
Anthropic
Labs
About This Page
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Known For
The ideas, systems, and research directions that make this person worth knowing.
01
Scaling laws
02
Training efficiency and compute tradeoffs
03
Large-scale model systems
04
Alignment via AI feedback (Constitutional AI)
05
Constitutional AI: Harmlessness from AI Feedback
06
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
Start Here
Canonical papers, project pages, or repositories that anchor this profile.
Rahul Patil joins Anthropic as Chief Technology OfficerarticleScaling Laws for Neural Language ModelspaperScaling Laws and Interpretability of Learning from Repeated DataarticleConstitutional AI: Harmlessness from AI FeedbackpaperTraining a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackpaper
Signature Works
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Supporting Sources
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Related Researchers
People worth exploring next because they share topics, labs, or source material with this profile.
5 sources
Useful for the seam between Anthropic’s earlier alignment papers and its later audit-oriented safety work, where interpretability and evaluation start feeding into deployment practice.
4 sources
A useful person to follow for the part of Anthropic that moved from assistant training into explicit behavior-discovery and evaluation work.
4 sources
Important because his work sits near the point where technical alignment, evaluation practice, and the public case for safer frontier-model deployment meet.
4 sources
Worth following for the evaluation side of Anthropic’s alignment program, especially where model-written tests are used to surface new behaviors quickly.
4 sources
A good person to follow if you care about the practical evaluation layer at Anthropic rather than only its highest-level alignment claims.