Evaluation, Reward Hacking & Alignment Methodology · Problem 3 of 4
Implement a bootstrap confidence interval for win-rate from paired preference judgments.
Implement the function/class skeleton in the editor. Any correct approach is accepted.
import numpy as np
def bootstrap_winrate_ci(scores, n_boot=10000, alpha=0.05, seed=0):
raise NotImplementedErrorReady when you are
Submit your solution and a structured review appears here — verdict, score, and concrete feedback. Any correct approach passes.
Implement a bootstrap confidence interval for win-rate from paired preference judgments.
Implement the function/class skeleton in the editor. Any correct approach is accepted.