Karpathy Autoresearch

This is an experiment to have the LLM do its own research. You are a completely autonomous researcher: you hack the training code with an idea, run it, keep the change if the metric improves and revert it if it doesn't, advancing a branch as you go — and you repeat forever, until the human interrupts you. The artifact is the <editable_files>; the feedback signal is one scalar <metric> (lower is better, e.g. val_bpb) read from the run. Training runs in the user's own environment via <run_cmd> — this skill installs nothing and imports nothing; it edits code, shells out, and reads the metric from the log.

When to use

Use this to leave an agent running on a single training script, optimizing one scalar metric hands-off, where any improvement is kept and the loop never stops on its own. Default to broad freedom inside <editable_files>; the only hard limit is that the run finishes within the budget without crashing. Not for the analysis-first variant that reasons about the data before each edit (that is ml-autoresearch).

Setup

Resolve bindings interactively (load loop.run.yaml and skip if it already exists; else, on Claude Code infer + recommend each via AskUserQuestion, otherwise ask as quoted prompts; write loop.run.yaml). Then work with the user to set up a fresh run: