RLHF & Preference Optimization
RLHF and preference optimization shape how models reason, prioritize, and respond, especially in ambiguous or high-risk scenarios.

Overview
Preference modeling translates human judgment into training signals that influence how models prioritize, respond, and reason. In practice, these signals determine not just output quality, but how models balance usefulness, safety, and domain expectations under real-world conditions.
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