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Preference for Divergence-Based Objective Functions
In designing objective functions, a structure that represents the difference between two probability distributions is often preferred. This form is conceptually advantageous because it can be interpreted as a divergence, which measures the 'distance' or difference between the two distributions. This contrasts with less ideal forms, such as the difference between a log-probability distribution and another arbitrary function, which lack this clear interpretation.

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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Components of Karim's Constrained Choice Problem
Formulating Karim's Constrained Optimization Problem
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Evaluating a Public Health Objective Function
Interpreting a Firm's Objective
Prediction via Optimization
Preference for Divergence-Based Objective Functions
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Normalizing a Function to Create a Probability Distribution
A machine learning model is being trained to generate outputs. Its behavior is described by a probability distribution
p_model(y|x), and the desired behavior is captured by a target data distributionp_data(y|x). The training process involves minimizing an objective function. Which of the following objective function structures is most desirable because it can be clearly interpreted as a measure of the 'distance' or difference between the two distributions?Critique of an Objective Function Formulation
Evaluating Objective Function Designs