A useful profile for the operational side of alignment work, especially where RL systems and evaluation loops have to be robust enough to support day-to-day model development.
Researcher Profile
Editor reviewedTristan Hume
Alignment via AI feedback (Constitutional AI)
Contributor to Anthropic's infrastructure and evaluation stack
A useful profile for the systems side of alignment work, especially where infrastructure choices and evaluation throughput determine what a lab can actually test.
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About This Page
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Known For
The ideas, systems, and research directions that make this person worth knowing.
01
Helpful and harmless assistant training
02
Constitutional AI
03
Model-written evaluations
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
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A good profile for the less public part of frontier-model progress, where pretraining quality, evaluation loops, and systems choices do a lot of the real work.
A strong profile for the engineering and product layer underneath early Anthropic alignment work, especially where human-feedback collection and evaluation infrastructure had to become real systems.
A good person to follow for the evaluation-heavy side of Anthropic alignment work, especially where early assistant training later feeds into reasoning-faithfulness and model-written testing.
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.
Useful for the evaluation-heavy side of Anthropic’s research, especially where the lab moved from RLHF and Constitutional AI into broader behavior discovery.