AI Skills & How They Work
Human in the Loop: RLHF and Beyond
Human-in-the-loop systems use people to guide, correct, or approve AI behavior, and RLHF is the training technique that turns human preferences into model behavior. The loop is not a safety net you add at the end; it is a design decision about where judgment lives. Done well, it catches failures cheaply; done poorly, it becomes theater.
Points clés
- RLHF trains on preferences: humans rank outputs, and the model learns to produce what people prefer, not necessarily what is true.
- Preference is not truth: RLHF improves helpfulness and tone but can reinforce confident, fluent wrong answers.
- The loop has three placements: training time (RLHF), runtime (review and approval), and post-hoc (feedback and retraining).
- Runtime review is the practical lever: require human approval for high-stakes actions, and make rejection easy and logged.
- Feedback quality matters: vague thumbs-up or thumbs-down signals are noisy; structured corrections teach more.
- Automation bias is real: humans rubber-stamp AI output under time pressure, so design for scrutiny, not just presence.
- Measure the loop: track override rates, time spent, and outcomes; a loop nobody uses is not a control.
- The honest limit: humans cannot review millions of outputs, so the loop must be selective and risk-based.
Questions fréquentes
Does RLHF make models safe?
It makes them more aligned with human preferences, which overlaps with safety but is not the same thing.
Where should humans sit in an AI workflow?
At irreversible, high-impact, or legally significant steps, and anywhere the cost of a wrong answer is high.
Can feedback improve a deployed model?
Yes through retraining or prompt updates, but only with clean, representative feedback and evals.
Is human review always worth it?
No. For high-volume, low-stakes tasks the cost exceeds the benefit; automate with monitoring instead.
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