KL divergence

One-line definition

For probability distributions $p$ and $q$ over the same space:

$$\mathrm{KL}(p\Vert q)=\sum _{x}p(x)\log \frac{p(x)}{q(x)}(\text{or }\int p\log (p/q)dx\text{ for continuous}).$$

It’s the expected log-ratio of $p$ to $q$ under $p$. Measuring how much information is lost using $q$ to encode samples from $p$.

Why it matters

KL divergence is the fundamental object of statistical learning. It connects:

• Maximum likelihood (minimizing $\mathrm{KL}({\hat{p}}_{\text{data}}\Vert p_{\theta })$).
• Variational inference (minimizing $\mathrm{KL}(q_{\varphi }\Vert p)$).
• Cross-entropy loss = entropy of data + KL.
• Information bottleneck and mutual information.
• Policy gradient methods in RL (TRPO, PPO use KL constraints).
• Knowledge distillation (student matches teacher distribution via KL).

Properties

• Non-negative: $\mathrm{KL}(p\Vert q)\ge 0$, with equality iff $p=q$ (Gibbs’ inequality).
• Asymmetric: $\mathrm{KL}(p\Vert q)\ne \mathrm{KL}(q\Vert p)$ in general. Choose direction based on whether you are “fitting $q$ to $p$” or vice versa.
• Not a metric: no triangle inequality, not symmetric.
• Infinite if $q(x)=0$ where $p(x)>0$: $q$ must cover the support of $p$.
• Information-theoretic: equals expected extra bits (or nats) per sample needed to encode $p$ using a code optimized for $q$.

Forward vs. reverse KL

The asymmetry matters in practice. For approximating $p$ with $q$:

• Forward KL, $\mathrm{KL}(p\Vert q)$: penalizes $q$ for missing modes of $p$ (“mean-seeking”. $q$ tries to cover all of $p$). Used in standard MLE.
• Reverse KL, $\mathrm{KL}(q\Vert p)$: penalizes $q$ for placing mass where $p$ has none (“mode-seeking”. $q$ collapses to one mode). Used in variational inference.

For a multimodal $p$, forward KL gives a broad average; reverse KL picks one mode. Visualize on a 2-Gaussian mixture: forward = average ellipse over both; reverse = one of the two.

Connection to cross-entropy

For empirical distribution $\hat{p}$ over a finite dataset:

$$H(\hat{p},p_{\theta })=-{\mathbb{E}}_{\hat{p}}[\log p_{\theta }]=H(\hat{p})+\mathrm{KL}(\hat{p}\Vert p_{\theta }).$$

Cross-entropy = entropy + KL. Since entropy of the data doesn’t depend on $\theta$, minimizing cross-entropy = minimizing KL = MLE. This is why “the loss is cross-entropy” and “we’re minimizing KL to the data” are the same statement.

Common usage in ML

Use case	Direction
Classification cross-entropy loss	Forward $\mathrm{KL}(\text{data}\Vert \text{model})$
Variational inference (ELBO)	Reverse $\mathrm{KL}(q\Vert p)$
RLHF / PPO penalty	Reverse $\mathrm{KL}({\pi }_{\theta }\Vert {\pi }_{\text{ref}})$. Keep new policy close to reference
Knowledge distillation	Forward $\mathrm{KL}(\text{teacher}\Vert \text{student})$ with temperature
t-SNE	Reverse $\mathrm{KL}(P\Vert Q)$ on pairwise similarities

Common pitfalls

• Computing KL between distributions with different supports. If $p(x)>0$ and $q(x)=0$, KL is $+\infty$.
• Confusing JS divergence (symmetric) with KL. GANs originally used JS; modern variants (Wasserstein) avoid both.
• Forgetting the asymmetry direction. Forward and reverse KL produce qualitatively different optimizers.
• Using KL on samples without density estimates. KL is between distributions, not between sample sets; sample-based estimators are noisy and biased.