🧮 ML Research Engineering · Experimentation Rigor

Design ablations that isolate contributions

One-factor-at-a-time with matched budgets separating real gains from tuning luck.

intermediate~35 minResearch EngineersML ScientistsPhD Researchers

Steps

1List claimed components; rank by novelty and implementation cost
2Baseline run repeated with 3+ seeds establishing variance floor
3Remove ONE component per run; keep all else byte-identical
4Match compute budgets across arms — bigger ablation runs cheat
5Report deltas WITH seed variance, not single-run point estimates
6Test interactions for top-2 components before final claims

Common Pitfalls

▲Ablations run with different hyperparameter sweeps
▲Seed cherry-picking turning noise into conclusions

Commands

Install with skills CLI

$ npx skills add aniruddhaadak80/skills --skill experimentation-rigor-ablation-design

Install globally

$ npx skills add aniruddhaadak80/skills --skill experimentation-rigor-ablation-design -g

Tags

#ablations#experimental-design#ml-research#experimentation-rigor

Install this skill

$ npx skills add aniruddhaadak80/skills --skill experimentation-rigor-ablation-design -a opencode

SKILL.md

# Design ablations that isolate contributions

> One-factor-at-a-time with matched budgets separating real gains from tuning luck.

## When to Use This Skill

One-factor-at-a-time with matched budgets separating real gains from tuning luck.

## Steps

1. List claimed components; rank by novelty and implementation cost
2. Baseline run repeated with 3+ seeds establishing variance floor
3. Remove ONE component per run; keep all else byte-identical
4. Match compute budgets across arms — bigger ablation runs cheat
5. Report deltas WITH seed variance, not single-run point estimates
6. Test interactions for top-2 components before final claims

## Common Pitfalls

- Ablations run with different hyperparameter sweeps
- Seed cherry-picking turning noise into conclusions

View source on GitHub ↗

Staged reproduction from inference-first to full retrain with deviation journals.

🧮 ML Research Engineering·~50m

Open →

Keep experiment tracking trustworthyfoundation

Every run reproducible from logged config + code version + data snapshot reference.

🧮 ML Research Engineering·~30m