Compare Reward-Model RLHF and Direct Preference Optimization

Contrast a standard RLHF setup that uses a reward model with Direct Preference Optimization. Describe the data, training phases, objectives, and the function of a reference policy for each method.

Constraints & Assumptions

Assume a common setup of supervised fine-tuning then preference learning. RLHF encompasses many variants; outline a typical reward-model plus policy-optimization pipeline without suggesting it’s the only one.

Clarifying Questions

What shape do the preferences have? Is a supervised model already available? Are responses gathered online or stored in a dataset? Which behaviors and evaluation metrics are meant to improve?

What a Strong Answer Covers

Follow the preference pairs through reward modeling or direct policy optimization, describe regularization toward a reference, and address data quality and evaluation beyond the training objective.

Follow-up Questions

Does DPO need a separately trained reward model? Does it do away with human preference data? How can preference bias, distribution shift, or reward gaming impact the final model?

Overview: Contrast reward-model RLHF with DPO across preference data, policy objectives, reference regularization, training stages, and evaluation under distribution shift.

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