March 18th, 2026. OpenAI posts the rules for something called Parameter Golf. Train the best language model that fits in a 16MB artifact and trains in under 10 minutes on 8xH100s. I read it twice and almost closed the tab. I trained a GAN a long time ago. I'd picked up transformer basics here and there. I had never trained a language model. I had never rented a GPU by the hour. Every word in the announcement was slightly beyond my reach. I decided to try anyway. A month later, my submission is open on OpenAI's repository at openai/parameter-golf#1747 — a hair behind the world record on the public leaderboard. What follows is that month, told as a map of the ideas I had to understand along the way: partial rotary embeddings, quantized weights, test-time training, and a handful of things I'd never heard of on March 17th. The rest of this series unpacks them one at a time. This post is about why the challenge itself became the best curriculum I've ever found for getting into modern language modeling.…