Directly optimizing pass@k for pruning outperforms separate quality and diversity strategies by naturally balancing both objectives, achieving better results especially when keeping moderate numbers of candidate solutions.
This paper tackles test-time reasoning efficiency by proposing a pruning method that directly optimizes pass@k—the probability of finding at least one correct solution. Rather than separately balancing solution quality and diversity, the method unifies both objectives through a single metric that naturally rewards good partial solutions while penalizing redundancy.