Compare Reward-Model RLHF and Direct Preference Optimization
Mistral AISoftware Engineer · Technical Screen
Last updated
Compare reward-model RLHF and DPO through preference data, policy objectives, reference regularization, training stages, and evaluation under distribution shift.
Compare a typical reward-model-based RLHF pipeline with Direct Preference Optimization. Explain the data, training stages, objectives, and role of a reference policy in each approach.
Constraints & Assumptions
Use supervised fine-tuning followed by preference learning as the common setting. RLHF is a broad family; describe a representative reward-model plus policy-optimization pipeline rather than implying there is only one implementation.
Clarifying Questions
What form do preferences take? Is there an existing supervised model? Are responses collected online or fixed in a dataset? What behavior and evaluation criteria should improve?
Follow-up questions
- Does DPO require a separately trained reward model? Does it eliminate human preference data? How might preference bias, distribution shift, or reward exploitation affect the final model?
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