The Statistical Benefits of Multiple Responses for Learning from Demonstrations
Many generative systems return multiple candidate responses and are evaluated according to the best one. Recent work shows that, when demonstrations are optimal, pass@$k$ can reduce the sample complexity of learning from demonstrations by a logarithmic factor in $k$. We ask what happens when the demonstrator is not assumed to be optimal. We find that multiple responses provide a qualitatively stronger benefit in this setting. In a finite reward-class model with no reward feedback, moving from pass@$1$ to any pass@$k$ with $k\ge2$ changes the worst-case dependence on target accuracy from $1/\varepsilon^2$ to $1/\varepsilon$, uniformly over demonstrator quality. Under standard evaluation, w...
| Authors | Chandramauli Chakraborty, Cong Ma |
|---|---|
| Primary category | stat.ML |
| Published | 27 September 2026 |
| Last updated at source | 13 October 2026 |
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