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Experiments

Experiments are the second leg of each tutorial. Keep them small enough to repeat on a limited budget while preserving interfaces that later methods can replace.

Experiment tiers

Tier Purpose Run mode
smoke Check that code and configuration are intact Run automatically on every pull request
repro Reproduce a key curve or conclusion Trigger manually and save a result summary
frontier Expensive training or large-model evaluation Run manually or overnight with a budget record

Directory convention

text experiments/<id>-<slug>/ README.md config.yaml results.md

Model weights, datasets, and complete logs do not enter Git. The repository keeps configurations, code, metrics, an index of failure examples, and environment information.