Collect side-by-side human rankings of alternative model outputs to fit a reward model is correct because reinforcement learning from human feedback requires an explicit reward model trained from human preference judgments before policy optimization begins.

Evaluators are shown alternative playlist or recommendation outputs for the same input and they indicate which output they prefer or how they rank them. Those pairwise comparisons and full rankings become the labeled targets used to fit a reward model which then scores candidate outputs during reinforcement learning. Training the reward model on human judgments aligns the optimization with subjective listener tastes rather than relying only on indirect engagement metrics.

Analyze historical clickstream and rating logs with statistical methods is insufficient because implicit engagement signals do not reliably provide the explicit pairwise labels that reward models for RLHF need. Historical logs can inform features and baselines but they do not replace deliberate human preference comparisons.

Amazon Personalize is not the right step because it is a managed recommendation service and it does not collect side by side human preference labels nor train reward models for RLHF. You can use it as a conventional recommendation solution but it does not substitute for the RLHF labeling workflow required before reinforcement training.

Generate synthetic reward scores for unlabeled examples using a pre-trained model is premature because pseudo labeling presupposes an existing trustworthy reward model or gold labels. Synthetic scores can introduce bias and they do not replace the initial human preference data that grounds the reward function.

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