Training a helpful, harmless, honest LLM requires three sequential stages that each build on the previous one. Here's how SFT, reward modeling, and RL fit together as a system — and where each stage can fail.
Human annotation doesn't scale to the data volumes modern alignment requires. Synthetic data — generated by LLMs, filtered, and refined — has become the dominant approach. Here's how it's done and where it breaks down.