Evaluate the benefit of increasing teacher and student sample counts

Determine whether increasing the teacher sample count K and the student candidate count N improves action-coverage in ActionOPD sufficiently to justify the additional generation and semantic-matching costs.

Background

ActionOPD uses a fixed number of teacher responses and student candidate responses at each student-encountered state. The paper notes that long-horizon interactions or states with many plausible decisions may yield few semantically matching actions under these fixed sample sets. Increasing K and N could improve the probability of covering relevant teacher-consistent actions, but would also increase generation and matching expense. The paper explicitly leaves the benefit of this increase unresolved in the reported experimental regime.