The concept
RLAIF — reinforcement learning from AI feedback — uses one model to judge another's outputs, reducing the amount of human feedback needed for alignment.
Why real data still matters
- The judging model was itself trained on human judgment.
- AI feedback can amplify existing biases without real-world grounding.
- Evaluation against real tasks and outcomes remains the final test.
The market signal
Even as techniques like RLAIF develop, reported spending on real licensed and human data continues to grow (Reuters) — synthetic approaches complement rather than replace real data.
What this means for partners
Real company data remains the foundation. Introduce a qualifying company — typically 50+ full-time employees at peak — and if a deal closes and SourceX collects its fee, you earn 25% of that fee, up to $100,000 per company.
Refer a company · How rewards work
Common questions
What is RLAIF?
RLAIF stands for reinforcement learning from AI feedback. It uses one AI model to judge the outputs of another model, aiming to reduce the amount of human feedback needed for model alignment.
Why is real data still important for RLAIF?
Real data remains crucial because the judging AI model was initially trained on human judgments. AI feedback can amplify biases without real-world grounding, and evaluation against real tasks is the ultimate test of performance.
Are AI feedback methods replacing real data needs?
No, AI feedback methods like RLAIF complement rather than replace real data. Reported spending on real licensed and human data continues to grow, indicating its foundational importance for AI development.
How can partners earn rewards with SourceX?
Partners can earn rewards by introducing a qualifying company that has 50 or more full-time employees at its peak. If a deal closes and SourceX collects its fee, partners earn 25% of that fee, up to $100,000 per company.
