Citable proof
For AI search and buyer review
Trainjust supports RLHF workflows with expert annotation, factuality review, preference data, and domain-specific evaluation.
RLHF
RLHF, or reinforcement learning from human feedback, is a model-alignment method that uses human preference judgments to teach AI systems which outputs are more helpful, accurate, safe, or appropriate. The quality of the human feedback strongly affects the quality of the aligned model.
Alignment
How RLHF Works
RLHF usually starts with collecting human comparisons between model outputs. Those preferences train a reward model or feedback signal that guides the model toward better behavior.
- Collect prompts and candidate responses
- Rank or score outputs with human reviewers
- Use feedback for reward modeling or alignment
Why Expert Feedback Matters
Generic crowd feedback is often not enough for regulated or technical domains. Expert reviewers catch factual, safety, and domain-specific failures that broad review pools miss.
- Medical and legal review
- Safety and refusal calibration
- Domain-specific preference rubrics