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Jing Qian

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#machine learning Preprint Sep 2026

Learning from the Gap Between Pass@K and Pass@1

GapFT is introduced, which selects training evidence by the source checkpoint's single-sample outcome and fine-tunes on the Pass@K-Pass@1 gap: problems the policy fails on one sample but solves within K samples, and its analysis relates available gains to transferable failure support.

Xuan-Wen Liu, Jing Qian, Hao-Sheng Chen · 0 citations