diff --git a/ax/models/torch/alebo.py b/ax/models/torch/alebo.py
index 12c615a743..e49cedeea7 100644
--- a/ax/models/torch/alebo.py
+++ b/ax/models/torch/alebo.py
@@ -30,11 +30,10 @@
 from botorch.models.gp_regression import FixedNoiseGP
 from botorch.models.gpytorch import GPyTorchModel
 from botorch.models.model_list_gp_regression import ModelListGP
-from botorch.optim.fit import fit_gpytorch_scipy
+from botorch.optim.fit import fit_gpytorch_mll_scipy
 from botorch.optim.initializers import initialize_q_batch_nonneg
-from botorch.optim.numpy_converter import module_to_array
+from botorch.optim.numpy_converter import _scipy_objective_and_grad, module_to_array
 from botorch.optim.optimize import optimize_acqf
-from botorch.optim.utils import _scipy_objective_and_grad
 from botorch.posteriors.gpytorch import GPyTorchPosterior
 from botorch.utils.datasets import SupervisedDataset
 from gpytorch.distributions.multivariate_normal import MultivariateNormal
@@ -283,12 +282,10 @@ def get_map_model(
             m.load_state_dict(init_state_dict)
         mll = ExactMarginalLogLikelihood(m.likelihood, m)
         mll.train()
-        mll, info_dict = fit_gpytorch_scipy(mll, track_iterations=False, method="tnc")
-        logger.debug(info_dict)
-        # pyre-fixme[58]: `<` is not supported for operand types
-        #  `Union[List[botorch.optim.fit.OptimizationIteration], float]` and `float`.
-        if info_dict["fopt"] < f_best:
-            f_best = float(info_dict["fopt"])  # pyre-ignore
+        result = fit_gpytorch_mll_scipy(mll, method="tnc")
+        logger.debug(result)
+        if result.fval < f_best:
+            f_best = float(result.fval)
             sd_best = m.state_dict()
     # Set the final value
     m = ALEBOGP(B=B, train_X=train_X, train_Y=train_Y, train_Yvar=train_Yvar)
diff --git a/ax/utils/testing/mock.py b/ax/utils/testing/mock.py
index 455d505906..a3aee87298 100644
--- a/ax/utils/testing/mock.py
+++ b/ax/utils/testing/mock.py
@@ -29,7 +29,6 @@ def one_iteration_minimize(*args, **kwargs):
             kwargs["options"] = {}
 
         kwargs["options"]["maxiter"] = 1
-
         return minimize(*args, **kwargs)
 
     # pyre-fixme[3]: Return type must be annotated.
@@ -58,7 +57,7 @@ def minimal_gen_os_ics(*args, **kwargs):
 
         mock_fit = es.enter_context(
             mock.patch(
-                "botorch.optim.fit.minimize",
+                "botorch.optim.core.minimize",
                 wraps=one_iteration_minimize,
             )
         )