Case Study: Designing an RLHF / Preference-Tuning Platform
The end-to-end system that turns human preferences into a monthly-shipped, aligned model
Model interview answer for the platform-engineering capstone behind every instruction-tuned LLM: prompt sourcing and privacy filtering, comparison-UI and annotator-calibration design, SFT to reward-model to policy-training pipelines (and the DPO variant that collapses two stages into one), reward-hacking-aware evaluation, and a system design where rollout generation on inference-engine workers, not the learner, is the dominant cost. Grounded in InstructGPT, Bradley-Terry, DPO, RLAIF and open-source RLHF-infra patterns.
Practice questions (6)
-
View →
Diagnosing a Reward Model That's Being Gamed
Advanced · Free -
View →
Choosing Between RLHF and DPO for a Mid-Size Team
Advanced · Free -
View →
A Vendor Cohort's Quality Is Degrading — Find It Before It Ships
Advanced -
View →
Capacity Planning: The Team Wants to Ship Biweekly
Advanced -
View →
Suspiciously Good Win-Rate — Check for Contamination
Advanced