An ML search loop that can't overfit its own eval — the gate is in code, not a prompt
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I kept seeing agent and eval demos where the honesty — held-out discipline, "no metric gaming" — lives in a prompt, or in a paper's methodology section. So I tried to build the opposite: a search loop where the anti-overfitting rules are enforced in executable code , then ran it against a real external grader (MLE-bench) to see whether that discipline actually costs you anything. The result is heldout — MIT, zero runtime deps, works as a Claude Code skill:…
1Key Takeaways
- I kept seeing agent and eval demos where the honesty — held-out discipline, "no metric gaming" — lives in a prompt, or in a paper's methodology section.
- So I tried to build the opposite: a search loop where the anti-overfitting rules are enforced in executable code , then ran it against a real external grader (MLE-bench) to see whether that discipline actually costs you anything.
- The result is heldout — MIT, zero runtime deps, works as a Claude Code skill:….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that i kept seeing agent and eval demos where the honesty — held-out discipline, "no metric gaming" — lives in a prompt, or in a paper's methodology section.
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