# The recipe is the news, not the model

> DeepSeek-R1 is not a secret model. It is an open recipe: RL on verifiable rewards, then distill. Frontier reasoning behavior just got cheap to copy.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-01-20

Canonical: https://anth.us/articles/deepseek-r1-recipe/

::image{
  src="images/posts/deepseek-r1-recipe.png"
  layout="center-small-image"
  alt="deepseek-ai/DeepSeek-R1 on GitHub: the open reasoning-model recipe"
}

[DeepSeek-R1](https://arxiv.org/abs/2501.12948) matches o1-class reasoning and then gives the method away. The headline people will remember is the leaderboard. The part that changes how you build systems is the recipe.

R1-Zero skips supervised fine-tuning and runs reinforcement learning with a verifier: right answer, not a human-written chain of thought. Group Relative Policy Optimization (GRPO) is the optimizer. Reasoning behaviors show up because they win the reward, not because someone demonstrated them. R1 then adds a cold start so the traces are readable. The distilled 32B Qwen model beats o1-mini on several math and code benches.

o1 showed that extra test-time compute is a product. R1 shows you can induce that behavior with RL on checks you already have, then shrink it. If your domain has a grader — tests, a rubric, a schema, a human vote — you are looking at a training loop, not a prompting trick. The expensive model is no longer the only place that kind of reasoning lives.