Supervised fine-tuning teaches a model from example outputs. Reinforcement learning (RL) teaches from *rewards* -- the model generates its own outputs, and a reward function scores them. The model ...

In tutorial 04, we wrote a GRPO training loop from scratch: sample completions, grade them, compute advantages, build datums, train. That works, but every new task would repeat the same boilerplate.