What is the primary purpose of preference learning in AI alignment?

• A. To generate synthetic data
• B. To infer reward models from human judgments.
• C. To reduce training time
• D. To optimize a loss function

Answer: (B)

Preference learning in AI alignment focuses on turning human judgments into guidance for the agent by inferring a reward model from comparisons of preferred outcomes. Humans compare two outputs or behaviors and indicate which they prefer; this feedback is used to train a model that assigns higher reward to preferred results. The learned reward model then becomes the target the agent aims to maximize, typically inside a reinforcement learning loop. This approach ties the agent’s goals to human values without needing explicit rules, making alignment more scalable for complex tasks. Generating synthetic data, reducing training time, or simply optimizing a generic loss function aren’t the primary aim here; the core purpose is to derive a reward signal from human judgments that guides behavior.