Article Series

Policy Optimization for LLMs: From Fundamentals to Production

This series takes you from the mathematical foundations of reinforcement learning through the practical algorithms used to align large language models. You will build intuition for why each method exists, what problems it solves, and how the field evolved from PPO to GRPO to GDPO.

4 parts · ~120 min total reading time · Last updated Feb 2026

Part 1

Reinforcement Learning Foundations for LLM Alignment

Master MDPs, policy gradients, value functions, and actor-critic methods — the building blocks every LLM alignment technique relies on.

Part 2

PPO for Language Models: The RLHF Workhorse

The clipped objective, trust regions, and why PPO's four-model architecture creates problems at scale.

Part 3

GRPO: Eliminating the Value Network

Replace the value network with group-relative advantages. 33% memory savings with competitive performance.

Part 4

GDPO: Multi-Reward RL Done Right

When multiple rewards collapse advantages, GDPO normalizes each independently for stable multi-objective optimization.

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