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Direct Pass@k optimization prunes reasoning candidates while preserving diversity

Researchers propose directly optimizing Pass@k for thought pruning, balancing candidate quality and diversity when discarding partial reasoning traces early to save test-time compute. Why it matters Better pruning could lower inference cost for reasoning models without sacrificing the chance that a…

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  1. 2026-10-25 06:58 · DEV Community — Machine Learning
    Direct Pass@k optimization prunes reasoning candidates while preserving diversity

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