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Generative AI Leader Practice Question: A data scientist wants to apply reinforcement…
A data scientist wants to apply reinforcement learning from human feedback (RLHF) to improve a chatbot's helpfulness. Which TWO steps are part of the RLHF process? (Select 2)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Collect human rankings or preferences on multiple model outputs
RLHF typically involves collecting human rankings of model outputs and then training a reward model to score outputs, which is used to fine-tune the model via PPO.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use prompt engineering to tune the model without retraining
Why it's wrong here
Prompt engineering is not part of RLHF.
- ✗
Collect human-annotated demonstrations of ideal responses
Why it's wrong here
That is supervised fine-tuning, not RLHF.
- ✓
Collect human rankings or preferences on multiple model outputs
Why this is correct
Human feedback is used to train a reward model.
- ✗
Deploy the model in A/B testing to gather implicit feedback
Why it's wrong here
Implicit feedback is not part of the standard RLHF pipeline; RLHF uses explicit human rankings.
- ✓
Train a reward model based on human preferences
Why this is correct
The reward model approximates human preferences to guide the reinforcement learning step.
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