diff --git a/n3fit/src/n3fit/hyper_optimization/rewards.py b/n3fit/src/n3fit/hyper_optimization/rewards.py
index fd9a0d965f..ae49a4559c 100644
--- a/n3fit/src/n3fit/hyper_optimization/rewards.py
+++ b/n3fit/src/n3fit/hyper_optimization/rewards.py
@@ -62,7 +62,7 @@ def _average_best(fold_losses: np.ndarray, percentage: float = 0.9, axis: int =
     -------
         float: The average along the specified axis.
     """
-    num_best = int(np.ceil(percentage * len(sorted_losses)))
+    num_best = int(np.ceil(percentage * len(fold_losses)))
 
     if np.isnan(fold_losses).any():
         log.warning(f"{np.isnan(fold_losses).sum()} replicas have NaNs losses")