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    <title>Probability on Nalar</title>
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    <description>Recent content in Probability on Nalar</description>
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    <lastBuildDate>Sun, 06 Sep 2026 00:00:00 +0700</lastBuildDate>
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      <title>Compare Model Distributions with KL Divergence</title>
      <link>https://nalar.dev/compare-model-distributions-with-kl-divergence/</link>
      <pubDate>Sun, 06 Sep 2026 00:00:00 +0700</pubDate>
      <guid>https://nalar.dev/compare-model-distributions-with-kl-divergence/</guid>
      <description>&lt;p&gt;AI systems often produce probability distributions rather than single answers. A classifier assigns probabilities to classes, a language model assigns probabilities to possible next tokens, and a teacher model can provide a soft target distribution for a smaller student. In all of these cases, developers need a way to ask: &lt;strong&gt;how different is one probability distribution from another?&lt;/strong&gt;&lt;/p&gt;&#xA;&lt;p&gt;&lt;strong&gt;Kullback-Leibler divergence&lt;/strong&gt;, usually shortened to &lt;strong&gt;KL divergence&lt;/strong&gt;, is one answer. It measures how much a comparison distribution &lt;code&gt;Q&lt;/code&gt; differs from a reference distribution &lt;code&gt;P&lt;/code&gt;, with the differences weighted by what &lt;code&gt;P&lt;/code&gt; considers important.&lt;/p&gt;</description>
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