facebookresearch/lilo — explained in plain English
Analysis updated 2026-07-17 · repo last pushed 2026-05-12
Tune machine learning hyperparameters using natural-language feedback instead of manual scoring functions.
Optimize a text summarizer's tradeoffs between accuracy, length, and readability by describing preferences in words.
Compare candidate designs (like product settings) by having an LLM judge which one better fits your stated goals.
| facebookresearch/lilo | aim-uofa/reasonmatch | airbone42/360-data-athlete | |
|---|---|---|---|
| Stars | 12 | 12 | 12 |
| Language | Python | Python | Python |
| Last pushed | 2026-05-12 | — | — |
| Maintenance | Maintained | — | — |
| Setup difficulty | moderate | hard | hard |
| Complexity | 4/5 | 5/5 | 4/5 |
| Audience | researcher | researcher | general |
Figures from each repo's GitHub metadata at analysis time.
Requires bringing your own LLM API key (OpenAI, Anthropic, etc.) to run experiments.
A Bayesian optimization tool that uses an LLM's plain-English judgments of candidate solutions to automatically find the option that best matches your stated preferences.
Mainly Python. The stack also includes Python, YAML.
Maintained — commit in last 6 months (last push 2026-05-12).
Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
double-check against the repo, no cap.