Beyond Standard RLHF
Traditional RLHF relies on crowdsourced feedback. That works for basic alignment, but a crowdworker can't tell a good answer from an excellent one in oncology or contract law. Expert RLHF uses feedback from the people who set the standards in those fields.
Our Approach
We work with professors from MIT, Stanford, Harvard, and other leading institutions to produce feedback that reflects real expertise:
Domain Expert Evaluations
Feedback from active researchers and practitioners who can spot the small differences between a good answer and a great one.
Preference Learning
Expert rankings of model outputs that teach the judgment calls and trade-offs each field expects.
Constitutional AI Guidance
Principles and constraints, written by experts, that keep model behavior in line with best practice in the field.
Iterative Refinement
Repeated rounds of expert feedback that bring the model closer to field standards each time.
The Difference
Models trained with expert RLHF avoid the obvious mistakes, and they also make the judgment calls an experienced practitioner would make. That makes them reliable enough for high-stakes applications.
Improve your model's judgment with expert feedback
Work with domain leaders who know what an expert-level answer looks like.
Get Started