Definition
Plain language
Giving a model just enough initial training to get off the ground before letting it learn the rest by trial and error.
As stated in the literature
A lightweight initialization stage (e.g., a small set of filtered successful demonstrations) that bootstraps basic competence so subsequent RL exploration isn't pure flailing; OpenWebRL deliberately keeps it minimal to preserve policy plasticity.
Also called: warm-start, warm-starting
Why it matters: It matters because a small head start gives a model enough basic competence to learn productively, while keeping it light preserves its room to improve.
For example, before letting a web agent learn by trial and error, you first show it a few hundred successful task recordings so it isn't clicking randomly from the start.
Heard on the show
“You take a weighted average of those past methods — weighted by similarity and by reward — and that becomes your warm-start prior for the new problem.”Episode 042 — An Agentic Scientific Computing System That Actually Remembers What It Learns