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Reproducible gains

As explained in Sutskever's List

Reproducible gains are performance improvements that persist against strong baselines under controlled and repeatable evaluation conditions.

Reproducible gains are performance improvements that survive comparison with strong baselines and controlled, repeatable evaluation. They count only when robustness travels across distribution shifts and new benchmarks, rather than ending as a single leaderboard win. section 7.6

When benchmark progress fails to travel

Progress on ARC-AGI-1 appeared rapid, but some of it reflected benchmark familiarity and overspecialized search. ARC-AGI-2 and ARC-AGI-3 sharpened the demand for evidence that transfers beyond one benchmark. section 7.6

How Sutskever's List covers it

How deep it goes
Defines reproducibility through strong baselines, controls, distribution shifts, and transfer, without prescribing a detailed experimental protocol.
Examples
Uses ARC-AGI-1, ARC-AGI-2, ARC-AGI-3, benchmark familiarity, overspecialized search, and leaderboard wins.
What you can do after
Evaluate AI claims by checking strong baselines, controlled conditions, distribution-shift robustness, and transfer to new benchmarks.
How it connects
Connects benchmarks with baselines, distribution shift, transfer, robustness, and leaderboard evaluation.
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This explainer was generated by Manning's knowledge graph from the text of Sutskever's List. The section links point to the parts of the book it draws on.