GSM1k Rebuild Exposes Benchmark Contamination: Up to 8-Point Drops, Agent Eval Advice
krishnan · x · 2026-11-02
Citing François Chollet's point that evolution optimizes for generality rather than benchmark scores, the author argues models overspecialize to environments and become fragile when those shift.
Evidence: Scale AI rebuilt GSM8K from scratch as GSM1k, and some model families dropped up to 8 points while frontier models barely moved — the gap only shows on problems the model has never seen, signaling contamination.
Practical takeaway for enterprise agents: don't score on leaderboards; evaluate on holdout data that mimics real drift, such as next month's tickets, schema changes, and policy updates.
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