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Verified 2026-08-14

Reproducibility checklist

A practical checklist for making ML/AI results redoable: data versions, code, seeds, hardware notes, eval scripts, and honest nondeterminism. It aligns with the NeurIPS paper checklist’s reproducibility / code-data / experimental-detail ...

What it is

A practical checklist for making ML/AI results redoable: data versions, code, seeds, hardware notes, eval scripts, and honest nondeterminism. It aligns with the NeurIPS paper checklist’s reproducibility / code-data / experimental-detail questions and with Pineau et al.’s NeurIPS 2019 reproducibility program report.

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HIGH PRIORITYINFOGRAPHIC / CHECKLIST
◷ IN PRODUCTION

Visual Spec & Architecture Diagram

Icon checklist card: code available, seeds fixed, hyperparameters listed, data versioned, hardware noted, eval script shared, random variance reported. Each item check/X. Title: 'Reproducibility checklist'.

Educational Focus: Converts abstract 'be reproducible' into scannable icons matching chapter.

Why it matters

Irreproducible claims waste years. Free education should model better habits. MLRC’s evolution into an official NeurIPS 2026 track signals that reproducibility is treated as science—not busywork.

How it works (plain)

  1. Pin dependencies and record commits.
  2. Record seeds and data cuts/manifests.
  3. Share scripts and exact commands.
  4. Note GPU nondeterminism; report ranges when needed.
  5. Prefer honest limits over fake exactness.

Everyday example

A classmate cannot rerun your notebook because the dataset path and torch version live only in your head—that is a checklist fail.

Try it

Apply the checklist to one of your labs; fix the top gap. Skim NeurIPS checklist items on experimental reproducibility and open access to data/code.

Myths

⚠️ Myth: Same code always same floats on GPU.
✓ Reality: Nondeterminism happens—report ranges.
⚠️ Myth: Releasing code alone makes a paper reproducible.
✓ Reality: Raff (NeurIPS 2019) argues code release is important but not sufficient; independent reimplementation studies matter.
⚠️ Myth: Negative reproduction results are worthless.
✓ Reality: MLRC 2026 explicitly values careful failures to reproduce.

Sources