E.4.6 Information Theory Formula Reference and Exercises

Prerequisites: This page summarizes all formulas in module E.4. It is best reviewed after reading E.4.1 through E.4.5. If this is your first pass, skip to the main articles first.

---

This page collects all formulas used in module E.4 for review. It is recommended that you read the previous articles first and then use this page as a reference table.

Information Theory Formulas You Will Meet in This Book

Concept	Formula	Meaning in reinforcement learning
Self-information	$I(x)=-\log p(x)$	Low-probability events contain more information
Entropy	$H(P)=-\sum_x p(x)\log p(x)$	Policy randomness and exploration
Entropy bonus	$J=\mathbb{E}[G]+\beta H(\pi)$	Encourages exploration and avoids premature certainty
Cross-entropy	$H(P,Q)=-\sum_x P(x)\log Q(x)$	Classification training and reward model training
KL divergence	$D_{KL}(P|Q)=\sum_x P(x)\log\frac{P(x)}{Q(x)}$	Measures differences between old and new policies
Cross-entropy-KL relationship	$D_{KL}(P|Q)=H(P,Q)-H(P)$	KL is extra encoding cost
KL penalty	$\text{reward}-\beta D_{KL}$	Constrains policy drift in PPO/RLHF
RLHF objective	$J(\pi)=\mathbb{E}\pi[r(x,y)]-\beta D{KL}(\pi_\theta|\pi_{ref})$	Reward maximization with a reference model constraint
DPO loss	$\mathcal{L}{DPO}=-\mathbb{E}[\log\sigma(\beta\log\frac{\pi\theta(y_w\mid x)}{\pi_{ref}(y_w\mid x)}-\beta\log\frac{\pi_\theta(y_l\mid x)}{\pi_{ref}(y_l\mid x)})]$	Uses preference data to optimize relative probabilities
Mutual information	$I(X;Y)=H(X)-H(X\mid Y)$	Whether a representation keeps task-relevant information

---

Summary

The hierarchy on this page is: start from "smaller probability means more information" and "a more uniform policy has higher entropy," then extend to cross-entropy, KL, the RLHF regularized objective, and the DPO loss. When reading complex information-theoretic formulas, first ask: is this measuring randomness, prediction error, or the distance between two policy distributions?

---

Common Mistakes

1. Treating entropy as noise. High entropy means the policy is more random and may help exploration, but it does not mean the policy is worse.
2. Treating KL as an ordinary distance. KL is asymmetric. $D_{KL}(P\|Q)$ and $D_{KL}(Q\|P)$ emphasize different errors.
3. Thinking the KL constraint is only mathematical decoration. In RLHF, the KL term directly determines how far the model can move from the reference model.

---

Exercises

1. Compare $[0.5,0.5]$ and $[0.9,0.1]$. Which has higher entropy? Why?
2. If the old policy is $[0.5,0.5]$ and the new policy is $[0.8,0.2]$, write the expanded expression for $D_{KL}(\pi_{old}\|\pi_{new})$.
3. In the RLHF objective $\mathbb{E}[r]-\beta D_{KL}$, when $\beta$ becomes larger, does the policy update become more aggressive or more conservative?