@@ -270,14 +270,8 @@ class LearnerND(BaseLearner):

                     self._subtriangulations[simpl].add_point(point)

     def ask(self, n=1):

-        # TODO make this method shorter, and nicer, it should be possible

-        xs = []

-        losses = []

-        for i in range(n):

-            x, loss = self._ask()

-            xs.append(*x)

-            losses.append(*loss)

-        return xs, losses

+        xs, losses = zip(*(self._ask() for _ in range(n)))

+        return list(xs), list(losses)

     def _ask(self, n=1):

         # Complexity: O(N log N)

@@ -296,7 +290,7 @@ class LearnerND(BaseLearner):

                 self._tell_pending(p)

         if n == 0:

-            return new_points, new_loss_improvements

+            return new_points[0], new_loss_improvements[0]

         losses = [(-v, k) for k, v in self.losses().items()]

         heapq.heapify(losses)

@@ -310,7 +304,7 @@ class LearnerND(BaseLearner):

             r = np.array([self._random.random() for _ in range(self.ndim)])

             p = r * a + b

             p = tuple(p)

-            return [p], [np.inf]

+            return p, np.inf

         while len(new_points) < n:

             if len(losses):

@@ -349,7 +343,7 @@ class LearnerND(BaseLearner):

             self._tell_pending(point_new, simplex)

-        return new_points, new_loss_improvements

+        return new_points[0], new_loss_improvements[0]

     def update_losses(self, to_delete: set, to_add: set):

         pending_points_unbound = set()  # TODO add the points outside the triangulation to this as well