Fully Hidden Test Sets Make Benchmarks Harder to Adopt, Researcher Notes
srchvrs · x · 2026-11-09
Responding to the claim that any benchmark with public test data will inevitably be overfit, the author adds a counterintuitive observation: benchmarks whose test data is fully hidden (not merely label-withheld) often struggle to gain adoption. One key reason, offered with a note of cynicism, is that such benchmarks are harder to optimize against.
Context from the thread:
- The original post argues overfitting to public test data is inevitable, whether conscious or not.
- Common patterns: selecting checkpoints by test score; the worst case, training on test data; and creative middle grounds like paraphrasing test data.
- The author stresses that "hidden" means the test data itself is fully hidden, not just the labels.
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