Post-training (RLHF, RLVR and after)
Where a raw predictor becomes an assistant — and, lately, a reasoner.
You're looking at a concept explainer: what this term means in plain English, and the records it ties together.
After pre-training, models are shaped: supervised fine-tuning on demonstrations, RLHF against human preferences, and now reinforcement learning on verifiable rewards (math checkers, passing tests) that produced the reasoning models. Post-training's share of total compute is rising sharply — labs describe it as the new frontier of differentiation, which is why data-labeling firms and RL environments became strategic assets.
How this connects to the rest of the stack
This record's links at a glance — click a group to jump to it.
Sources
DeepSeek-R1 (RL for reasoning) ↗
Record reviewed as of 2026-01-15 — see Methodology.