RLHF vs DPO: aligning models without breaking them

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-rlhf-and-dpo-comparison

1. What is the main idea of "RLHF vs DPO: aligning models without breaking them"?

   1. Compare reinforcement learning from human feedback and direct preference optimization at the level of intuition, not equations.
   2. Use AI as the final authority for the whole decision
   3. Avoid checking the answer once it sounds polished
   4. Focus only on speed instead of judgment

2. Which concept is most central to "RLHF vs DPO: aligning models without breaking them"?

   1. reward model
   2. preference data
   3. policy update
   4. alignment tax

3. Which use of AI fits this topic best?

   1. Settle which approach is best for every use case.
   2. Let the AI decide what matters without your review
   3. Sketch the data flow for RLHF and for DPO.
   4. Use the answer before checking whether it fits the situation

4. Which limitation should you watch for in this topic?

   1. Sketch the data flow for RLHF and for DPO.
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Settle which approach is best for every use case.

5. What should a careful learner remember about "RLHF vs DPO trade-offs"?

   1. Use AI to draft or organize ideas about preference data, then verify before acting.
   2. Skip the context so the tool can guess faster
   3. Treat the output as private even after sharing it online
   4. Use the answer without checking the source

6. You want to use AI after this lesson. What is the safest next step?

   1. Act immediately because the AI answer is written clearly
   2. Use AI for drafting and comparison, but verify before publishing or relying on it.
   3. Hide uncertainty so the final answer looks cleaner
   4. Use private or sensitive details before checking permission

7. How should AI output about preference data be treated?

   1. As proof that no other source is needed
   2. As a replacement for context, consent, or expert review
   3. As a draft or helper output that still needs human judgment and verification
   4. As something that becomes correct when it sounds confident

8. Name one way to verify an AI answer about preference data.

9. Which action would help you apply "RLHF vs DPO: aligning models without breaking them" responsibly?

   1. Eliminate the underlying difficulty of preference collection.
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Compare practical trade-offs: stability, cost, throughput.

10. Which choice is a bad use of AI for this lesson?

    1. Eliminate the underlying difficulty of preference collection.
    2. Sketch the data flow for RLHF and for DPO.
    3. Ask for a plain-language explanation of reward model
    4. Compare the answer with a trusted source