Preference tuning as a weekly loop: rater and AI labels, Bradley–Terry reward models, DPO and PPO with a KL leash, vLLM rollouts, reward-hacking checks.
Machine-learning engineers designing data, training and model systems.
Your approach: Explain the data, learning or model lifecycle in the brief. Support relevant quality and operating targets with evidence.
Design an RLHF and Preference-Tuning Pipeline. Preference tuning as a weekly loop: rater and AI labels, Bradley–Terry reward models, DPO and PPO with a KL leash, vLLM rollouts, reward-hacking checks. Work from the scoping questions below. State assumptions for any unspecified load, guarantee or target, then trace your design end to end. Explain one difficult case and a credible alternative; the worked example is a reference, not a required implementation.
Resolve the scoping questions for an RLHF and Preference-Tuning Pipeline. Separate stated behavior from assumptions, and identify what is outside your design.
Declare relevant volume, latency, freshness, quality or cost targets with units. Show calculations or an evaluation plan that can test them; unspecified targets are your assumptions, not hidden pass criteria.
Explain how your guarantees hold in a difficult case relevant to this subject. Address: One objective? Code and math?
Identify users, required behavior and exclusions. Answer: Model and scale? How often?
Define the information owned by the system and the inputs, outputs and errors at its boundaries. Resolve: AI labels allowed? Compute?
Estimate the dominant workload and resource demand with units and explicit assumptions. For a learned system, also state how quality is measured and what data is available.
Draw or describe the responsibilities needed for an RLHF and Preference-Tuning Pipeline. Trace a representative request, event or job from its input to a visible result; identify durable state owners.
Walk through a difficult case step by step, including detection and recovery. Consider: What must ship? One objective? Code and math?
Compare a credible alternative using your chosen workload and guarantees. Explain a remaining risk, a signal to watch and when you would change the design.