Leaderboard Incentives: Model Rankings under Strategic Post-Training
Influential benchmarks incentivize competing model developers to strategically allocate post-training resources toward improvements on the leaderboard, a phenomenon dubbed benchmaxxing or training on the test task. In this work, we initiate a principled study of the incentive structure that benchmarks induce. We model benchmarking as a Stackelberg game between a benchmark designer who chooses an evaluation protocol and multiple model developers who compete simultaneously in a subgame given by the designer's choice. Each competitor has a model of unknown latent quality and can inflate its observed score by allocating resources to benchmark-specific improvements. First, we prove that curren...
| Authors | Yatong Chen, Guanhua Zhang, Moritz Hardt |
|---|---|
| Primary category | cs.GT |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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