Case Study: Designing an RLHF / Preference-Tuning Platform

Every LLM vendor, and a growing number of enterprises fine-tuning open-weight models for their own product, runs a version of the same expensive, recurring pipeline: collect judgments about which of two model outputs is better, turn those judgments into a training signal, and use that signal to nudge the model toward the preferred behavior — then do it again next month with a new base model, a new eval bar, and a team that has learned a dozen new lessons about what breaks. It is, for most of these organizations, the single most expensive ML pipeline they operate, and it runs on a cadence, not a one-off. The interview prompt: "Design the end-to-end system that turns human preferences into an aligned model — collection, reward modelling, RL training, evaluation and iteration — for a team shipping a new model version monthly."

This is deliberately a different question from two others in this catalog that sound adjacent. The Fine-Tuning: SFT, LoRA/QLoRA, RLHF and DPO subject teaches the methods — what SFT, LoRA, RLHF and DPO change mechanically inside a model. The PPO & Modern Policy Optimization subject teaches the algorithm — why the clipped surrogate objective keeps a policy-gradient step from collapsing the policy. Neither teaches the platform: the data pipeline that produces the preference data these methods consume, the infrastructure that runs the algorithm at production scale, the versioning discipline that makes a monthly release traceable, and the capacity plan that keeps annotators, compute and eval harnesses all pointed at the same release date. This subject assumes you already know what RLHF, DPO and PPO are and focuses entirely on what it takes to run them, monthly, as a system with SLAs, an on-call rotation, and a budget.

Aim to deliver the whole answer in about 40 minutes, following the standard case-study arc: clarify requirements, business objective to ML objective, framing, data, model pipeline, evaluation, system design, then a scalability deep-dive and cost model.