HardTechnical screen30 min to answer

Compare supervised fine-tuning, preference optimization, and reinforcement learning from feedback.

1Give yourself 30 minutes

2Answer out loud, not in your head

3Then compare with the answer below

Stuck? Show a way to structure it+

01Clarify the objective, user, constraints, and acceptable failure modes.
02Structure the response around objectives, data, stability, alignment trade-offs.
03Compare credible alternatives and state the decision criteria.
04Finish with validation, monitoring, and what would change the decision.

Reference answer

strong answer

I would first define the outcome and acceptable error boundary. I would explicitly cover objectives, data, stability, alignment trade-offs. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

objectives
data
stability
alignment trade-offs

Must be adapted to the interviewer’s scenario
Would benefit from concrete numbers and personal examples

alternative answer

A second valid approach is to begin with the highest-risk assumption and test it cheaply. I would explicitly cover objectives, data, stability, alignment trade-offs. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

objectives
data
stability
alignment trade-offs

Must be adapted to the interviewer’s scenario
Would benefit from concrete numbers and personal examples

Grade yourself

objectives

weight 30%

StrongExplains objectives concretely and ties it to a decision or measurable outcome.

WeakMentions objectives without implications, trade-offs, or evidence.

data

weight 25%

StrongExplains data concretely and ties it to a decision or measurable outcome.

WeakMentions data without implications, trade-offs, or evidence.

stability

weight 25%

StrongExplains stability concretely and ties it to a decision or measurable outcome.

WeakMentions stability without implications, trade-offs, or evidence.

alignment trade-offs

weight 20%

StrongExplains alignment trade-offs concretely and ties it to a decision or measurable outcome.

WeakMentions alignment trade-offs without implications, trade-offs, or evidence.

Then expect these follow-ups

What assumption is most fragile?
Tests: depth
What alternative did you reject and why?
Tests: adaptability
How would you validate this in production?
Tests: adaptability

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