Ask-E: An Environment for Calibrated Question Generation
Today, we improve models by training and evaluating them on problems at the frontier of their abilities. Creating such problems is itself a demanding task, requiring the ability to probe model limits and generalize beyond existing question distributions. It also means placing problems at a precise difficulty level, which requires understanding what it takes to solve them. In short, generating problems calibrated to a model's current frontier demands capability beyond it, an increasingly burdensome constraint as models improve. Our key insight is that we can leverage this constraint to our advantage: a model that can generate problems consistently calibrated to a given frontier must posses...
| Authors | Sarah Pratt, Jae Sung Park, Scott Geng et al. |
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| Primary category | cs.CL |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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