diff --git a/ax/adapter/tests/test_base_adapter.py b/ax/adapter/tests/test_base_adapter.py
index 97716a13993..6e4e5eed757 100644
--- a/ax/adapter/tests/test_base_adapter.py
+++ b/ax/adapter/tests/test_base_adapter.py
@@ -76,7 +76,7 @@
 from botorch.exceptions.warnings import InputDataWarning
 from botorch.models.utils.assorted import validate_input_scaling
 from pandas.testing import assert_frame_equal
-from pyre_extensions import none_throws
+from pyre_extensions import assert_is_instance, none_throws
 
 ADAPTER__GEN_PATH: str = "ax.adapter.base.Adapter._gen"
 
@@ -908,8 +908,14 @@ def test_set_model_space(self) -> None:
             .index.get_level_values("arm_name")
         )
         self.assertEqual(set(ood_arms), {"status_quo", "custom"})
-        self.assertEqual(m.model_space.parameters["x1"].lower, -5.0)  # pyre-ignore[16]
-        self.assertEqual(m.model_space.parameters["x2"].upper, 15.0)  # pyre-ignore[16]
+        self.assertEqual(
+            assert_is_instance(m.model_space.parameters["x1"], RangeParameter).lower,
+            -5.0,
+        )
+        self.assertEqual(
+            assert_is_instance(m.model_space.parameters["x2"], RangeParameter).upper,
+            15.0,
+        )
         self.assertEqual(len(m.model_space.parameter_constraints), 1)
 
         # With expand model space, custom is not OOD, and model space is expanded
@@ -925,8 +931,14 @@ def test_set_model_space(self) -> None:
             .index.get_level_values("arm_name")
         )
         self.assertEqual(set(ood_arms), {"status_quo"})
-        self.assertEqual(m.model_space.parameters["x1"].lower, -20.0)
-        self.assertEqual(m.model_space.parameters["x2"].upper, 18.0)
+        self.assertEqual(
+            assert_is_instance(m.model_space.parameters["x1"], RangeParameter).lower,
+            -20.0,
+        )
+        self.assertEqual(
+            assert_is_instance(m.model_space.parameters["x2"], RangeParameter).upper,
+            18.0,
+        )
         self.assertEqual(m.model_space.parameter_constraints, [])
 
         # With fill values, SQ is also in design, and x2 is further expanded
@@ -941,7 +953,10 @@ def test_set_model_space(self) -> None:
                 transform_configs={"FillMissingParameters": {"fill_values": sq_vals}},
             )
         self.assertEqual(sum(m.training_in_design), 7)
-        self.assertEqual(m.model_space.parameters["x2"].upper, 20)
+        self.assertEqual(
+            assert_is_instance(m.model_space.parameters["x2"], RangeParameter).upper,
+            20,
+        )
         self.assertEqual(m.model_space.parameter_constraints, [])
 
         # Using parameter backfill values
@@ -955,7 +970,10 @@ def test_set_model_space(self) -> None:
             search_space=ss,
         )
         self.assertEqual(sum(m.training_in_design), 7)
-        self.assertEqual(m.model_space.parameters["x2"].upper, 20)
+        self.assertEqual(
+            assert_is_instance(m.model_space.parameters["x2"], RangeParameter).upper,
+            20,
+        )
         self.assertEqual(m.model_space.parameter_constraints, [])
 
         # Check log scale expansion with OOD trial having parameter value == 0
@@ -992,12 +1010,12 @@ def test_set_model_space(self) -> None:
 
         # Assert that the expanded model space did not include 0.0
         self.assertEqual(
-            m.model_space.parameters["x1"].lower,
+            assert_is_instance(m.model_space.parameters["x1"], RangeParameter).lower,
             0.0001,
         )
         # x2 model space should still be expanded
         self.assertEqual(
-            m.model_space.parameters["x2"].upper,
+            assert_is_instance(m.model_space.parameters["x2"], RangeParameter).upper,
             2.0,
         )
 
diff --git a/ax/adapter/tests/test_cross_validation.py b/ax/adapter/tests/test_cross_validation.py
index 55908746e1d..5001bdbf608 100644
--- a/ax/adapter/tests/test_cross_validation.py
+++ b/ax/adapter/tests/test_cross_validation.py
@@ -9,6 +9,7 @@
 import warnings
 from collections.abc import Iterable
 from itertools import product
+from typing import cast
 from unittest import mock
 
 import numpy as np
@@ -70,6 +71,7 @@
 from gpytorch.distributions import MultivariateNormal
 from linear_operator.operators import DiagLinearOperator
 from pandas import DataFrame
+from pyre_extensions import assert_is_instance
 
 # Number of in-design points created by _create_adapter_with_out_of_design_points()
 _OOD_ADAPTER_IN_DESIGN_COUNT = 3
@@ -78,9 +80,8 @@
 class CrossValidationTest(TestCase):
     def setUp(self) -> None:
         super().setUp()
-        # pyre-ignore [9] Pyre is too picky with union types.
         parameterizations: list[TParameterization] = [
-            {"x": x} for x in [2.0, 2.0, 3.0, 4.0]
+            cast(TParameterization, {"x": x}) for x in [2.0, 2.0, 3.0, 4.0]
         ]
         means = [[2.0, 4.0], [3.0, 5.0], [7.0, 8.0], [9.0, 10.0]]
         sems = [[1.0, 2.0], [1.0, 2.0], [1.0, 2.0], [1.0, 2.0]]
@@ -894,29 +895,27 @@ def test_efficient_loo_cv_with_fully_bayesian_model(self) -> None:
         experiment = get_branin_experiment(with_batch=True, with_completed_batch=True)
 
         # Create adapter with SaasFullyBayesianSingleTaskGP
+        generator = BoTorchGenerator(
+            surrogate=Surrogate(
+                surrogate_spec=SurrogateSpec(
+                    model_configs=[
+                        ModelConfig(
+                            botorch_model_class=SaasFullyBayesianSingleTaskGP,
+                        )
+                    ],
+                ),
+            )
+        )
         adapter = TorchAdapter(
             experiment=experiment,
-            generator=BoTorchGenerator(
-                surrogate=Surrogate(
-                    surrogate_spec=SurrogateSpec(
-                        model_configs=[
-                            ModelConfig(
-                                botorch_model_class=SaasFullyBayesianSingleTaskGP,
-                            )
-                        ],
-                    ),
-                )
-            ),
+            generator=generator,
             transforms=[UnitX],
         )
 
         # We need to mock the MCMC fitting to avoid running actual NUTS sampling
         # which is very slow. Instead, we'll inject mock MCMC samples.
-        surrogate = adapter.generator.surrogate  # pyre-ignore[16]
-        model = surrogate.model
-
-        # Verify the model is a SaasFullyBayesianSingleTaskGP
-        self.assertIsInstance(model, SaasFullyBayesianSingleTaskGP)
+        surrogate = generator.surrogate
+        model = assert_is_instance(surrogate.model, SaasFullyBayesianSingleTaskGP)
 
         # Get training data shape info
         train_X = model.train_inputs[0]
diff --git a/ax/adapter/tests/test_prediction_utils.py b/ax/adapter/tests/test_prediction_utils.py
index f72c20fe070..080cfe47015 100644
--- a/ax/adapter/tests/test_prediction_utils.py
+++ b/ax/adapter/tests/test_prediction_utils.py
@@ -83,14 +83,11 @@ def test_predict_by_features(self) -> None:
 
     @mock.patch("ax.adapter.random.RandomAdapter.predict")
     @mock.patch("ax.adapter.random.RandomAdapter")
-    # pyre-fixme[3]: Return type must be annotated.
     def test_predict_by_features_with_non_predicting_model(
         self,
-        # pyre-fixme[2]: Parameter must be annotated.
-        adapter_mock,
-        # pyre-fixme[2]: Parameter must be annotated.
-        predict_mock,
-    ):
+        adapter_mock: mock.MagicMock,
+        predict_mock: mock.MagicMock,
+    ) -> None:
         ax_client = _set_up_client_for_get_model_predictions_no_next_trial()
         _attach_completed_trials(ax_client)
 
diff --git a/ax/adapter/tests/test_random_adapter.py b/ax/adapter/tests/test_random_adapter.py
index 1cb4b4522a6..689c7ef121b 100644
--- a/ax/adapter/tests/test_random_adapter.py
+++ b/ax/adapter/tests/test_random_adapter.py
@@ -23,6 +23,7 @@
 from ax.exceptions.core import SearchSpaceExhausted
 from ax.generators.random.base import RandomGenerator
 from ax.generators.random.sobol import SobolGenerator
+from ax.generators.types import TConfig
 from ax.utils.common.testutils import TestCase
 from ax.utils.testing.core_stubs import (
     get_data,
@@ -45,7 +46,7 @@ def setUp(self) -> None:
         ]
         self.search_space = SearchSpace(self.parameters, parameter_constraints)
         self.experiment = Experiment(search_space=self.search_space)
-        self.model_gen_options = {"option": "yes"}
+        self.model_gen_options: TConfig = {"option": "yes"}
 
     def test_fit(self) -> None:
         adapter = RandomAdapter(experiment=self.experiment, generator=RandomGenerator())
@@ -79,10 +80,6 @@ def test_gen_w_constraints(self) -> None:
                 pending_observations={},
                 fixed_features=ObservationFeatures({"z": 3.0}),
                 optimization_config=None,
-                # pyre-fixme[6]: For 6th param expected `Optional[Dict[str,
-                # Union[None, Dict[str, typing.Any], OptimizationConfig,
-                # AcquisitionFunction, float, int, str]]]` but got `Dict[str,
-                # str]`.
                 model_gen_options=self.model_gen_options,
             )
         gen_args = mock_gen.mock_calls[0][2]
@@ -129,10 +126,6 @@ def test_gen_simple(self) -> None:
                 pending_observations={},
                 fixed_features=ObservationFeatures({}),
                 optimization_config=None,
-                # pyre-fixme[6]: For 6th param expected `Optional[Dict[str,
-                # Union[None, Dict[str, typing.Any], OptimizationConfig,
-                # AcquisitionFunction, float, int, str]]]` but got `Dict[str,
-                # str]`.
                 model_gen_options=self.model_gen_options,
             )
         gen_args = mock_gen.mock_calls[0][2]
diff --git a/ax/adapter/tests/test_torch_adapter.py b/ax/adapter/tests/test_torch_adapter.py
index 8c5288c865c..56d52e30271 100644
--- a/ax/adapter/tests/test_torch_adapter.py
+++ b/ax/adapter/tests/test_torch_adapter.py
@@ -632,7 +632,7 @@ def test_convert_experiment_data(self) -> None:
                 ordinal_features=[2],
                 discrete_choices={2: list(range(0, 11))},
                 task_features=[2] if use_task else [],
-                target_values={2: 0} if use_task else {},  # pyre-ignore
+                target_values={2: 0.0} if use_task else {},
             )
             converted_datasets, ordered_outcomes, _ = adapter._convert_experiment_data(
                 experiment_data=experiment_data,
diff --git a/ax/adapter/tests/test_torch_moo_adapter.py b/ax/adapter/tests/test_torch_moo_adapter.py
index 6e23fb669b4..b01c3dbcd2e 100644
--- a/ax/adapter/tests/test_torch_moo_adapter.py
+++ b/ax/adapter/tests/test_torch_moo_adapter.py
@@ -322,8 +322,7 @@ def test_hypervolume(self, _, cuda: bool = False) -> None:
             )
             for trial in exp.trials.values():
                 trial.mark_running(no_runner_required=True).mark_completed()
-            # pyre-fixme[16]: Optional type has no attribute `metrics`.
-            metrics_dict = exp.optimization_config.metrics
+            metrics_dict = none_throws(exp.optimization_config).metrics
             # Objective thresholds and synthetic observations chosen to have closed-form
             # hypervolumes to test.
             objective_thresholds = [
diff --git a/ax/adapter/tests/test_utils.py b/ax/adapter/tests/test_utils.py
index a4051f12e17..30853afd2d5 100644
--- a/ax/adapter/tests/test_utils.py
+++ b/ax/adapter/tests/test_utils.py
@@ -85,7 +85,6 @@ def test_extract_outcome_constraints(self) -> None:
             OutcomeConstraint(metric=Metric("m1"), op=ComparisonOp.LEQ, bound=0)
         ]
         res = extract_outcome_constraints(outcome_constraints, outcomes)
-        # pyre-fixme[16]: Optional type has no attribute `__getitem__`.
         self.assertEqual(res[0].shape, (1, 3))
         self.assertListEqual(list(res[0][0]), [1, 0, 0])
         self.assertEqual(res[1][0][0], 0)
@@ -137,10 +136,8 @@ def test_extract_objective_thresholds(self) -> None:
             outcomes=outcomes,
         )
         expected_obj_t_not_nan = np.array([2.0, 3.0, 4.0])
-        # pyre-fixme[16]: Optional type has no attribute `__getitem__`.
         self.assertTrue(np.array_equal(obj_t[:3], expected_obj_t_not_nan[:3]))
         self.assertTrue(np.isnan(obj_t[-1]))
-        # pyre-fixme[16]: Optional type has no attribute `shape`.
         self.assertEqual(obj_t.shape[0], 4)
 
         # Returns NaN for objectives without a threshold.
diff --git a/ax/adapter/transforms/tests/test_base_transform.py b/ax/adapter/transforms/tests/test_base_transform.py
index e89cec7080f..c2e932daeef 100644
--- a/ax/adapter/transforms/tests/test_base_transform.py
+++ b/ax/adapter/transforms/tests/test_base_transform.py
@@ -18,6 +18,7 @@
 from ax.core.objective import Objective
 from ax.core.observation import Observation, ObservationData, ObservationFeatures
 from ax.core.optimization_config import OptimizationConfig
+from ax.core.types import TParameterization
 from ax.utils.common.testutils import TestCase
 from ax.utils.testing.core_stubs import get_branin_experiment
 
@@ -66,10 +67,10 @@ def test_TransformObservations(self) -> None:
         means = np.array([3.0, 4.0])
         metric_signatures = ["a", "b"]
         covariance = np.array([[1.0, 2.0], [3.0, 4.0]])
-        parameters = {"x": 1.0, "y": "cat"}
+        parameters: TParameterization = {"x": 1.0, "y": "cat"}
         arm_name = "armmy"
         observation = Observation(
-            features=ObservationFeatures(parameters=parameters),  # pyre-ignore
+            features=ObservationFeatures(parameters=parameters),
             data=ObservationData(
                 metric_signatures=metric_signatures, means=means, covariance=covariance
             ),
diff --git a/ax/adapter/transforms/tests/test_cast_transform.py b/ax/adapter/transforms/tests/test_cast_transform.py
index 0dc6940f10c..d41de7085ee 100644
--- a/ax/adapter/transforms/tests/test_cast_transform.py
+++ b/ax/adapter/transforms/tests/test_cast_transform.py
@@ -35,6 +35,7 @@
 )
 from pandas import DataFrame
 from pandas.testing import assert_frame_equal
+from pyre_extensions import none_throws
 
 
 class CastTransformTest(TestCase):
@@ -179,8 +180,7 @@ def test_transform_observation_features_HSS(self) -> None:
                 self.assertIn(p_name, obsf.parameters)
             # Check that full parameterization is recorded in metadata
             self.assertEqual(
-                # pyre-fixme[16]: Optional type has no attribute `get`.
-                obsf.metadata.get(Keys.FULL_PARAMETERIZATION),
+                none_throws(obsf.metadata).get(Keys.FULL_PARAMETERIZATION),
                 self.obs_feats_hss.parameters,
             )
 
@@ -197,7 +197,7 @@ def test_transform_observation_features_HSS(self) -> None:
                 self.assertIn(p_name, obsf.parameters)
             # Check that full parameterization is recorded in metadata
             self.assertEqual(
-                obsf.metadata.get(Keys.FULL_PARAMETERIZATION),
+                none_throws(obsf.metadata).get(Keys.FULL_PARAMETERIZATION),
                 self.obs_feats_hss.parameters,
             )
 
@@ -245,8 +245,7 @@ def test_untransform_observation_features_HSS(self) -> None:
             },
         )
         self.assertEqual(
-            # pyre-fixme[16]: Optional type has no attribute `get`.
-            obsf.metadata.get(Keys.FULL_PARAMETERIZATION),
+            none_throws(obsf.metadata).get(Keys.FULL_PARAMETERIZATION),
             self.obs_feats_hss.parameters,
         )
 
@@ -264,7 +263,7 @@ def test_untransform_observation_features_HSS(self) -> None:
             },
         )
         self.assertEqual(
-            obsf.metadata.get(Keys.FULL_PARAMETERIZATION),
+            none_throws(obsf.metadata).get(Keys.FULL_PARAMETERIZATION),
             self.obs_feats_hss_2.parameters,
         )
 
diff --git a/ax/adapter/transforms/tests/test_choice_encode_transform.py b/ax/adapter/transforms/tests/test_choice_encode_transform.py
index 834d6714f80..5bf8ed02208 100644
--- a/ax/adapter/transforms/tests/test_choice_encode_transform.py
+++ b/ax/adapter/transforms/tests/test_choice_encode_transform.py
@@ -247,8 +247,9 @@ def test_hss_dependents_are_preserved(self) -> None:
         # x0 should be untouched because it's a fixed parameter.
         self.assertIsInstance(hss.parameters["x0"], FixedParameter)
         self.assertEqual(hss.parameters["x0"].parameter_type, ParameterType.BOOL)
-        # pyre-ignore[16] # Pyre doesn't understand fixed parameters have `.value`
-        self.assertEqual(hss.parameters["x0"].value, True)
+        self.assertEqual(
+            assert_is_instance(hss.parameters["x0"], FixedParameter).value, True
+        )
         self.assertEqual(hss.parameters["x0"].dependents, {True: ["x1", "x2"]})
 
         self.assertFalse(hss.parameters["x1"].is_hierarchical)
diff --git a/ax/adapter/transforms/tests/test_int_range_to_choice_transform.py b/ax/adapter/transforms/tests/test_int_range_to_choice_transform.py
index fee4d0bab9d..34d9ae1944c 100644
--- a/ax/adapter/transforms/tests/test_int_range_to_choice_transform.py
+++ b/ax/adapter/transforms/tests/test_int_range_to_choice_transform.py
@@ -72,7 +72,7 @@ def test_num_choices(self) -> None:
                 "e", lower=3, upper=5, parameter_type=ParameterType.INT
             ),
         }
-        search_space = SearchSpace(parameters=parameters.values())  # pyre-ignore[6]
+        search_space = SearchSpace(parameters=list(parameters.values()))
 
         # Don't specify max_choices (should be set to inf)
         t = IntRangeToChoice(search_space=search_space)
diff --git a/ax/adapter/transforms/tests/test_logit_transform.py b/ax/adapter/transforms/tests/test_logit_transform.py
index 38fb28afc8f..1c567c79a76 100644
--- a/ax/adapter/transforms/tests/test_logit_transform.py
+++ b/ax/adapter/transforms/tests/test_logit_transform.py
@@ -18,6 +18,7 @@
 from ax.utils.common.testutils import TestCase
 from ax.utils.testing.core_stubs import get_experiment_with_observations
 from pandas.testing import assert_frame_equal, assert_series_equal
+from pyre_extensions import assert_is_instance
 from scipy.special import expit, logit
 
 
@@ -107,16 +108,19 @@ def test_InvalidSettings(self) -> None:
     def test_TransformSearchSpace(self) -> None:
         ss2 = deepcopy(self.search_space)
         ss2 = self.t.transform_search_space(ss2)
-        # pyre-fixme[16]: `Parameter` has no attribute `lower`.
-        self.assertEqual(ss2.parameters["x"].lower, logit(0.9))
-        # pyre-fixme[16]: `Parameter` has no attribute `upper`.
-        self.assertEqual(ss2.parameters["x"].upper, logit(0.999))
+        self.assertEqual(
+            assert_is_instance(ss2.parameters["x"], RangeParameter).lower, logit(0.9)
+        )
+        self.assertEqual(
+            assert_is_instance(ss2.parameters["x"], RangeParameter).upper, logit(0.999)
+        )
         t2 = Logit(search_space=self.search_space_with_target)
         ss_target = deepcopy(self.search_space_with_target)
         t2.transform_search_space(ss_target)
         self.assertEqual(ss_target.parameters["x"].target_value, logit(0.123))
-        self.assertEqual(ss_target.parameters["x"].lower, logit(0.1))
-        self.assertEqual(ss_target.parameters["x"].upper, logit(0.3))
+        x_param = assert_is_instance(ss_target.parameters["x"], RangeParameter)
+        self.assertEqual(x_param.lower, logit(0.1))
+        self.assertEqual(x_param.upper, logit(0.3))
 
     def test_transform_experiment_data(self) -> None:
         parameterizations = [
diff --git a/ax/adapter/transforms/tests/test_metrics_as_task_transform.py b/ax/adapter/transforms/tests/test_metrics_as_task_transform.py
index 44472179380..dd287754588 100644
--- a/ax/adapter/transforms/tests/test_metrics_as_task_transform.py
+++ b/ax/adapter/transforms/tests/test_metrics_as_task_transform.py
@@ -14,6 +14,7 @@
 from ax.core.parameter import ChoiceParameter
 from ax.utils.common.testutils import TestCase
 from ax.utils.testing.core_stubs import get_search_space_for_range_values
+from pyre_extensions import assert_is_instance
 
 
 class MetricsAsTaskTransformTest(TestCase):
@@ -125,10 +126,10 @@ def test_TransformSearchSpace(self) -> None:
         self.assertEqual(len(new_ss.parameters), 3)
         new_param = new_ss.parameters["METRIC_TASK"]
         self.assertIsInstance(new_param, ChoiceParameter)
+        new_param_choice = assert_is_instance(new_param, ChoiceParameter)
         self.assertEqual(
-            # pyre-fixme[16]: `Parameter` has no attribute `values`.
-            new_param.values,
+            new_param_choice.values,
             ["TARGET", "metric1", "metric2"],
         )
-        self.assertTrue(new_param.is_task)  # pyre-ignore
+        self.assertTrue(new_param_choice.is_task)
         self.assertEqual(new_param.target_value, "TARGET")
diff --git a/ax/adapter/transforms/tests/test_one_hot_transform.py b/ax/adapter/transforms/tests/test_one_hot_transform.py
index fb7f41accf4..f685c6b6d67 100644
--- a/ax/adapter/transforms/tests/test_one_hot_transform.py
+++ b/ax/adapter/transforms/tests/test_one_hot_transform.py
@@ -25,6 +25,7 @@
 from ax.utils.testing.core_stubs import get_experiment_with_observations
 from pandas import DataFrame
 from pandas.testing import assert_frame_equal
+from pyre_extensions import assert_is_instance
 
 
 class OneHotTransformTest(TestCase):
@@ -130,10 +131,18 @@ def test_TransformSearchSpace(self) -> None:
         self.assertEqual(ss2.parameters["d"].parameter_type, ParameterType.FLOAT)
 
         # Parameter range fixed to [0,1].
-        # pyre-fixme[16]: `Parameter` has no attribute `lower`.
-        self.assertEqual(ss2.parameters["b" + OH_PARAM_INFIX + "0"].lower, 0.0)
-        # pyre-fixme[16]: `Parameter` has no attribute `upper`.
-        self.assertEqual(ss2.parameters["b" + OH_PARAM_INFIX + "1"].upper, 1.0)
+        self.assertEqual(
+            assert_is_instance(
+                ss2.parameters["b" + OH_PARAM_INFIX + "0"], RangeParameter
+            ).lower,
+            0.0,
+        )
+        self.assertEqual(
+            assert_is_instance(
+                ss2.parameters["b" + OH_PARAM_INFIX + "1"], RangeParameter
+            ).upper,
+            1.0,
+        )
         self.assertEqual(ss2.parameters["c"].parameter_type, ParameterType.BOOL)
 
         # Ensure we error if we try to transform a fidelity parameter
diff --git a/ax/adapter/transforms/tests/test_task_encode_transform.py b/ax/adapter/transforms/tests/test_task_encode_transform.py
index 42e95d25723..8a985685c32 100644
--- a/ax/adapter/transforms/tests/test_task_encode_transform.py
+++ b/ax/adapter/transforms/tests/test_task_encode_transform.py
@@ -13,6 +13,7 @@
 from ax.core.parameter import ChoiceParameter, ParameterType, RangeParameter
 from ax.core.search_space import SearchSpace
 from ax.utils.common.testutils import TestCase
+from pyre_extensions import assert_is_instance
 
 
 class TaskChoiceToIntTaskChoiceTransformTest(TestCase):
@@ -73,8 +74,9 @@ def test_TransformSearchSpace(self) -> None:
         self.assertEqual(ss2.parameters["b"].parameter_type, ParameterType.FLOAT)
         self.assertEqual(ss2.parameters["c"].parameter_type, ParameterType.INT)
 
-        # pyre-fixme[16]: `Parameter` has no attribute `values`.
-        self.assertEqual(ss2.parameters["c"].values, [0, 1])
+        self.assertEqual(
+            assert_is_instance(ss2.parameters["c"], ChoiceParameter).values, [0, 1]
+        )
         self.assertEqual(ss2.parameters["c"].target_value, 0)
         self.assertEqual(ss2.parameters["c"].dependents, {0: ["b"]})
 
diff --git a/ax/core/tests/test_batch_trial.py b/ax/core/tests/test_batch_trial.py
index 8a2c201f4b9..473f2ac68c6 100644
--- a/ax/core/tests/test_batch_trial.py
+++ b/ax/core/tests/test_batch_trial.py
@@ -212,11 +212,11 @@ def test_BatchLifecycle(self) -> None:
                 self.experiment.trial_indices_by_status[TrialStatus.STAGED], {0}
             )
             self.assertTrue(
-                # pyre-fixme[6]: For 1st param expected `Iterable[object]` but got
-                #  `bool`.
-                all(len(idcs) == 0)
-                for status, idcs in self.experiment.trial_indices_by_status.items()
-                if status != TrialStatus.STAGED
+                all(
+                    len(idcs) == 0
+                    for status, idcs in self.experiment.trial_indices_by_status.items()
+                    if status != TrialStatus.STAGED
+                )
             )
             self.assertIsNotNone(self.batch.time_staged)
             self.assertTrue(self.batch.status.is_deployed)
@@ -240,11 +240,11 @@ def test_BatchLifecycle(self) -> None:
                 self.experiment.trial_indices_by_status[TrialStatus.RUNNING], {0}
             )
             self.assertTrue(
-                # pyre-fixme[6]: For 1st param expected `Iterable[object]` but got
-                #  `bool`.
-                all(len(idcs) == 0)
-                for status, idcs in self.experiment.trial_indices_by_status.items()
-                if status != TrialStatus.RUNNING
+                all(
+                    len(idcs) == 0
+                    for status, idcs in self.experiment.trial_indices_by_status.items()
+                    if status != TrialStatus.RUNNING
+                )
             )
             self.assertIsNotNone(self.batch.time_run_started)
             self.assertTrue(self.batch.status.expecting_data)
@@ -261,11 +261,11 @@ def test_BatchLifecycle(self) -> None:
                 self.experiment.trial_indices_by_status[TrialStatus.COMPLETED], {0}
             )
             self.assertTrue(
-                # pyre-fixme[6]: For 1st param expected `Iterable[object]` but got
-                #  `bool`.
-                all(len(idcs) == 0)
-                for status, idcs in self.experiment.trial_indices_by_status.items()
-                if status != TrialStatus.COMPLETED
+                all(
+                    len(idcs) == 0
+                    for status, idcs in self.experiment.trial_indices_by_status.items()
+                    if status != TrialStatus.COMPLETED
+                )
             )
             self.assertIsNotNone(self.batch.time_completed)
             self.assertTrue(self.batch.status.is_terminal)
@@ -296,11 +296,11 @@ def test_BatchLifecycle(self) -> None:
                 self.experiment.trial_indices_by_status[TrialStatus.CANDIDATE], {0}
             )
             self.assertTrue(
-                # pyre-fixme[6]: For 1st param expected `Iterable[object]` but got
-                #  `bool`.
-                all(len(idcs) == 0)
-                for status, idcs in self.experiment.trial_indices_by_status.items()
-                if status != TrialStatus.CANDIDATE
+                all(
+                    len(idcs) == 0
+                    for status, idcs in self.experiment.trial_indices_by_status.items()
+                    if status != TrialStatus.CANDIDATE
+                )
             )
 
     def test_AbandonBatchTrial(self) -> None:
@@ -592,11 +592,9 @@ def test_get_candidate_metadata_from_all_generator_runs(self) -> None:
         # Check that if we add cand. metadata to gr_2, it will appear in cand.
         # metadata for the batch.
         gr_3 = get_generator_run2()
-        new_cand_metadata = {
+        new_cand_metadata: dict[str, dict[str, str] | None] | None = {
             a.signature: {"md_key": f"md_val_{a.signature}"} for a in gr_3.arms
         }
-        # pyre-fixme[8]: Attribute has type `Optional[Dict[str, Optional[Dict[str,
-        #  typing.Any]]]]`; used as `Dict[str, Dict[str, str]]`.
         gr_3._candidate_metadata_by_arm_signature = new_cand_metadata
         self.batch.add_generator_run(gr_3)
         gr_3 = self.batch._generator_runs[-1]
diff --git a/ax/core/tests/test_generator_run.py b/ax/core/tests/test_generator_run.py
index 9360c93293f..ed3b22900d8 100644
--- a/ax/core/tests/test_generator_run.py
+++ b/ax/core/tests/test_generator_run.py
@@ -17,6 +17,7 @@
     get_optimization_config,
     get_search_space,
 )
+from pyre_extensions import none_throws
 
 
 GENERATOR_RUN_STR = "GeneratorRun(3 arms, total weight 3.0)"
@@ -31,7 +32,7 @@ def setUp(self) -> None:
         self.search_space = get_search_space()
 
         self.arms = get_arms()
-        self.weights = [2, 1, 1]
+        self.weights: list[float] = [2, 1, 1]
         self.unweighted_run = GeneratorRun(
             arms=self.arms,
             optimization_config=self.optimization_config,
@@ -42,8 +43,6 @@ def setUp(self) -> None:
         )
         self.weighted_run = GeneratorRun(
             arms=self.arms,
-            # pyre-fixme[6]: For 2nd param expected `Optional[List[float]]` but got
-            #  `List[int]`.
             weights=self.weights,
             optimization_config=self.optimization_config,
             search_space=self.search_space,
@@ -56,13 +55,13 @@ def setUp(self) -> None:
 
     def test_Init(self) -> None:
         self.assertEqual(
-            # pyre-fixme[16]: Optional type has no attribute `outcome_constraints`.
-            len(self.unweighted_run.optimization_config.outcome_constraints),
+            len(
+                none_throws(self.unweighted_run.optimization_config).outcome_constraints
+            ),
             len(self.optimization_config.outcome_constraints),
         )
         self.assertEqual(
-            # pyre-fixme[16]: Optional type has no attribute `parameters`.
-            len(self.unweighted_run.search_space.parameters),
+            len(none_throws(self.unweighted_run.search_space).parameters),
             len(self.search_space.parameters),
         )
         self.assertEqual(str(self.unweighted_run), GENERATOR_RUN_STR)
@@ -120,8 +119,6 @@ def test_ModelPredictions(self) -> None:
         )
         run_no_model_predictions = GeneratorRun(
             arms=self.arms,
-            # pyre-fixme[6]: For 2nd param expected `Optional[List[float]]` but got
-            #  `List[int]`.
             weights=self.weights,
             optimization_config=get_optimization_config(),
             search_space=get_search_space(),
@@ -150,8 +147,6 @@ def test_ParamDf(self) -> None:
     def test_BestArm(self) -> None:
         generator_run = GeneratorRun(
             arms=self.arms,
-            # pyre-fixme[6]: For 2nd param expected `Optional[List[float]]` but got
-            #  `List[int]`.
             weights=self.weights,
             optimization_config=get_optimization_config(),
             search_space=get_search_space(),
@@ -166,8 +161,6 @@ def test_GenMetadata(self) -> None:
         gm = {"hello": "world"}
         generator_run = GeneratorRun(
             arms=self.arms,
-            # pyre-fixme[6]: For 2nd param expected `Optional[List[float]]` but got
-            #  `List[int]`.
             weights=self.weights,
             optimization_config=get_optimization_config(),
             search_space=get_search_space(),
@@ -178,14 +171,10 @@ def test_GenMetadata(self) -> None:
     def test_Sortable(self) -> None:
         generator_run1 = GeneratorRun(
             arms=self.arms,
-            # pyre-fixme[6]: For 2nd param expected `Optional[List[float]]` but got
-            #  `List[int]`.
             weights=self.weights,
         )
         generator_run2 = GeneratorRun(
             arms=self.arms,
-            # pyre-fixme[6]: For 2nd param expected `Optional[List[float]]` but got
-            #  `List[int]`.
             weights=self.weights,
         )
         self.assertTrue(generator_run1 < generator_run2)
diff --git a/ax/core/tests/test_objective.py b/ax/core/tests/test_objective.py
index 57d21c66714..6e28cc39b09 100644
--- a/ax/core/tests/test_objective.py
+++ b/ax/core/tests/test_objective.py
@@ -64,8 +64,7 @@ def test_Init(self) -> None:
 
     def test_MultiObjective(self) -> None:
         with self.assertRaises(NotImplementedError):
-            # pyre-fixme[7]: Expected `None` but got `Metric`.
-            return self.multi_objective.metric
+            self.multi_objective.metric
 
         self.assertEqual(self.multi_objective.metrics, list(self.metrics.values()))
         minimizes = [obj.minimize for obj in self.multi_objective.objectives]
@@ -106,8 +105,7 @@ def test_MultiObjective(self) -> None:
 
     def test_ScalarizedObjective(self) -> None:
         with self.assertRaises(NotImplementedError):
-            # pyre-fixme[7]: Expected `None` but got `Metric`.
-            return self.scalarized_objective.metric
+            self.scalarized_objective.metric
 
         self.assertEqual(
             self.scalarized_objective.metrics, [self.metrics["m1"], self.metrics["m2"]]
diff --git a/ax/core/tests/test_observation.py b/ax/core/tests/test_observation.py
index c18ccf25deb..abbc19cea32 100644
--- a/ax/core/tests/test_observation.py
+++ b/ax/core/tests/test_observation.py
@@ -35,22 +35,19 @@
 
 class ObservationsTest(TestCase):
     def test_ObservationFeatures(self) -> None:
-        t = np.datetime64("now")
+        t = pd.Timestamp.now()
+        obsf = ObservationFeatures(
+            parameters={"x": 0, "y": "a"},
+            trial_index=2,
+            start_time=t,
+            end_time=t,
+        )
         attrs = {
             "parameters": {"x": 0, "y": "a"},
             "trial_index": 2,
             "start_time": t,
             "end_time": t,
         }
-        # pyre-fixme[6]: For 1st param expected `Dict[str, Union[None, bool, float,
-        #  int, str]]` but got `Union[Dict[str, Union[int, str]], int, datetime64]`.
-        # pyre-fixme[6]: For 1st param expected `Optional[Dict[str, typing.Any]]`
-        #  but got `Union[Dict[str, Union[int, str]], int, datetime64]`.
-        # pyre-fixme[6]: For 1st param expected `Optional[int64]` but got
-        #  `Union[Dict[str, Union[int, str]], int, datetime64]`.
-        # pyre-fixme[6]: For 1st param expected `Optional[Timestamp]` but got
-        #  `Union[Dict[str, Union[int, str]], int, datetime64]`.
-        obsf = ObservationFeatures(**attrs)
         for k, v in attrs.items():
             self.assertEqual(getattr(obsf, k), v)
         printstr = (
@@ -58,29 +55,21 @@ def test_ObservationFeatures(self) -> None:
             f"start_time={t}, end_time={t})"
         )
         self.assertEqual(repr(obsf), printstr)
-        # pyre-fixme[6]: For 1st param expected `Dict[str, Union[None, bool, float,
-        #  int, str]]` but got `Union[Dict[str, Union[int, str]], int, datetime64]`.
-        # pyre-fixme[6]: For 1st param expected `Optional[Dict[str, typing.Any]]`
-        #  but got `Union[Dict[str, Union[int, str]], int, datetime64]`.
-        # pyre-fixme[6]: For 1st param expected `Optional[int64]` but got
-        #  `Union[Dict[str, Union[int, str]], int, datetime64]`.
-        # pyre-fixme[6]: For 1st param expected `Optional[Timestamp]` but got
-        #  `Union[Dict[str, Union[int, str]], int, datetime64]`.
-        obsf2 = ObservationFeatures(**attrs)
+        obsf2 = ObservationFeatures(
+            parameters={"x": 0, "y": "a"},
+            trial_index=2,
+            start_time=t,
+            end_time=t,
+        )
         self.assertEqual(hash(obsf), hash(obsf2))
         a = {obsf, obsf2}
         self.assertEqual(len(a), 1)
         self.assertEqual(obsf, obsf2)
-        attrs.pop("trial_index")
-        # pyre-fixme[6]: For 1st param expected `Dict[str, Union[None, bool, float,
-        #  int, str]]` but got `Union[Dict[str, Union[int, str]], int, datetime64]`.
-        # pyre-fixme[6]: For 1st param expected `Optional[Dict[str, typing.Any]]`
-        #  but got `Union[Dict[str, Union[int, str]], int, datetime64]`.
-        # pyre-fixme[6]: For 1st param expected `Optional[int64]` but got
-        #  `Union[Dict[str, Union[int, str]], int, datetime64]`.
-        # pyre-fixme[6]: For 1st param expected `Optional[Timestamp]` but got
-        #  `Union[Dict[str, Union[int, str]], int, datetime64]`.
-        obsf3 = ObservationFeatures(**attrs)
+        obsf3 = ObservationFeatures(
+            parameters={"x": 0, "y": "a"},
+            start_time=t,
+            end_time=t,
+        )
         self.assertNotEqual(obsf, obsf3)
         self.assertFalse(obsf == 1)
 
@@ -105,12 +94,9 @@ def test_ObservationFeaturesFromArm(self) -> None:
         self.assertEqual(obsf.trial_index, 3)
 
     def test_UpdateFeatures(self) -> None:
-        parameters = {"x": 0, "y": "a"}
-        new_parameters = {"z": "foo"}
+        parameters: TParameterization = {"x": 0, "y": "a"}
+        new_parameters: TParameterization = {"z": "foo"}
 
-        # pyre-fixme[6]: For 1st param expected `Dict[str, Union[None, bool, float,
-        #  int, str]]` but got `Dict[str, Union[int, str]]`.
-        # pyre-fixme[6]: For 2nd param expected `Optional[int64]` but got `int`.
         obsf = ObservationFeatures(parameters=parameters, trial_index=3)
 
         # Ensure None trial_index doesn't override existing value
@@ -119,8 +105,6 @@ def test_UpdateFeatures(self) -> None:
 
         # Test override
         new_obsf = ObservationFeatures(
-            # pyre-fixme[6]: For 1st param expected `Dict[str, Union[None, bool,
-            #  float, int, str]]` but got `Dict[str, str]`.
             parameters=new_parameters,
             trial_index=4,
             start_time=pd.Timestamp("2005-02-25"),
@@ -133,16 +117,19 @@ def test_UpdateFeatures(self) -> None:
         self.assertEqual(obsf.end_time, pd.Timestamp("2005-02-26"))
 
     def test_ObservationData(self) -> None:
+        metric_signatures = ["a", "b"]
+        means = np.array([4.0, 5.0])
+        covariance = np.array([[1.0, 4.0], [3.0, 6.0]])
+        obsd = ObservationData(
+            metric_signatures=metric_signatures,
+            means=means,
+            covariance=covariance,
+        )
         attrs = {
-            "metric_signatures": ["a", "b"],
-            "means": np.array([4.0, 5.0]),
-            "covariance": np.array([[1.0, 4.0], [3.0, 6.0]]),
+            "metric_signatures": metric_signatures,
+            "means": means,
+            "covariance": covariance,
         }
-        # pyre-fixme[6]: For 1st param expected `List[str]` but got
-        #  `Union[List[str], ndarray]`.
-        # pyre-fixme[6]: For 1st param expected `ndarray` but got `Union[List[str],
-        #  ndarray]`.
-        obsd = ObservationData(**attrs)
         self.assertEqual(obsd.metric_signatures, attrs["metric_signatures"])
         self.assertTrue(np.array_equal(obsd.means, attrs["means"]))
         self.assertTrue(np.array_equal(obsd.covariance, attrs["covariance"]))
@@ -258,19 +245,18 @@ def test_ObservationsFromData(self) -> None:
             },
         ]
         arms = {
-            # pyre-fixme[6]: For 1st param expected `Optional[str]` but got
-            #  `Union[Dict[str, Union[int, str]], float, str]`.
-            # pyre-fixme[6]: For 2nd param expected `Dict[str, Union[None, bool,
-            #  float, int, str]]` but got `Union[Dict[str, Union[int, str]], float,
-            #  str]`.
-            obs["arm_name"]: Arm(name=obs["arm_name"], parameters=obs["parameters"])
+            assert_is_instance(obs["arm_name"], str): Arm(
+                name=assert_is_instance(obs["arm_name"], str),
+                parameters=assert_is_instance(obs["parameters"], dict),
+            )
             for obs in truth
         }
         experiment = Mock()
         experiment._trial_indices_by_status = {status: set() for status in TrialStatus}
         trials = {
             obs["trial_index"]: Trial(
-                experiment, GeneratorRun(arms=[arms[obs["arm_name"]]])
+                experiment,
+                GeneratorRun(arms=[arms[assert_is_instance(obs["arm_name"], str)]]),
             )
             for obs in truth
         }
@@ -375,8 +361,7 @@ def test_ObservationsFromMapData(self) -> None:
         arms = [
             Arm(
                 name=assert_is_instance(obs["arm_name"], str),
-                # pyre-fixme[6]: For 2nd param expected `Dict[str, Union[None, bool,...
-                parameters=obs["parameters"],
+                parameters=assert_is_instance(obs["parameters"], dict),
             )
             for obs in truth
         ]
@@ -419,10 +404,18 @@ def test_ObservationsFromMapData(self) -> None:
             self.assertEqual(obs.features.trial_index, t["trial_index"])
             self.assertEqual(obs.data.metric_signatures, [t["metric_name"]])
             self.assertEqual(obs.data.metric_signatures, [t["metric_signature"]])
-            # pyre-fixme[6]: For 2nd argument expected `Union[_SupportsArray[dtype[ty...
-            self.assertTrue(np.array_equal(obs.data.means, t["mean_t"]))
-            # pyre-fixme[6]: For 2nd argument expected `Union[_SupportsArray[dtype[ty...
-            self.assertTrue(np.array_equal(obs.data.covariance, t["covariance_t"]))
+            self.assertTrue(
+                np.array_equal(
+                    obs.data.means,
+                    np.asarray(assert_is_instance(t["mean_t"], np.ndarray)),
+                )
+            )
+            self.assertTrue(
+                np.array_equal(
+                    obs.data.covariance,
+                    np.asarray(assert_is_instance(t["covariance_t"], np.ndarray)),
+                )
+            )
             self.assertEqual(obs.arm_name, t["arm_name"])
             self.assertEqual(obs.features.metadata, {"step": t["step"]})
 
@@ -514,37 +507,28 @@ def test_ObservationsFromDataAbandoned(self) -> None:
             },
         ]
         arms = {
-            # pyre-fixme[6]: For 1st param expected `Optional[str]` but got
-            #  `Union[Dict[str, Union[float, str]], Dict[str, Union[int, str]], float,
-            #  ndarray, str]`.
-            # pyre-fixme[6]: For 2nd param expected `Dict[str, Union[None, bool,
-            #  float, int, str]]` but got `Union[Dict[str, Union[float, str]],
-            #  Dict[str, Union[int, str]], float, ndarray, str]`.
-            obs["arm_name"]: Arm(name=obs["arm_name"], parameters=obs["parameters"])
+            assert_is_instance(obs["arm_name"], str): Arm(
+                name=assert_is_instance(obs["arm_name"], str),
+                parameters=assert_is_instance(obs["parameters"], dict),
+            )
             for obs in truth
         }
         experiment = Mock()
         experiment._trial_indices_by_status = {status: set() for status in TrialStatus}
-        trials = {
-            obs["trial_index"]: (
-                Trial(experiment, GeneratorRun(arms=[arms[obs["arm_name"]]]))
+        trials: dict[int, Trial | BatchTrial] = {
+            assert_is_instance(obs["trial_index"], int): (
+                Trial(
+                    experiment,
+                    GeneratorRun(arms=[arms[assert_is_instance(obs["arm_name"], str)]]),
+                )
             )
             for obs in truth[:-1]
-            # pyre-fixme[16]: Item `Dict` of `Union[Dict[str, typing.Union[float,
-            #  str]], Dict[str, typing.Union[int, str]], float, ndarray, str]` has no
-            #  attribute `startswith`.
-            if not obs["arm_name"].startswith("2")
+            if not assert_is_instance(obs["arm_name"], str).startswith("2")
         }
         batch = BatchTrial(experiment, GeneratorRun(arms=[arms["2_0"], arms["2_1"]]))
-        # pyre-fixme[6]: For 1st param expected
-        #  `SupportsKeysAndGetItem[Union[Dict[str, Union[float, str]], Dict[str,
-        #  Union[int, str]], float, ndarray, str], Trial]` but got `Dict[int,
-        #  BatchTrial]`.
-        trials.update({2: batch})
-        # pyre-fixme[16]: Optional type has no attribute `mark_abandoned`.
-        trials.get(1).mark_abandoned()
-        # pyre-fixme[16]: Optional type has no attribute `mark_arm_abandoned`.
-        trials.get(2).mark_arm_abandoned(arm_name="2_1")
+        trials[2] = batch
+        none_throws(trials.get(1)).mark_abandoned()
+        assert_is_instance(trials.get(2), BatchTrial).mark_arm_abandoned(arm_name="2_1")
         type(experiment).arms_by_name = PropertyMock(return_value=arms)
         type(experiment).trials = PropertyMock(return_value=trials)
         type(experiment).metrics = PropertyMock(
@@ -627,19 +611,18 @@ def test_ObservationsFromDataWithSomeMissingTimes(self) -> None:
             },
         ]
         arms = {
-            # pyre-fixme[6]: For 1st param expected `Optional[str]` but got
-            #  `Union[None, Dict[str, Union[int, str]], float, str]`.
-            # pyre-fixme[6]: For 2nd param expected `Dict[str, Union[None, bool,
-            #  float, int, str]]` but got `Union[None, Dict[str, Union[int, str]],
-            #  float, str]`.
-            obs["arm_name"]: Arm(name=obs["arm_name"], parameters=obs["parameters"])
+            assert_is_instance(obs["arm_name"], str): Arm(
+                name=assert_is_instance(obs["arm_name"], str),
+                parameters=assert_is_instance(obs["parameters"], dict),
+            )
             for obs in truth
         }
         experiment = Mock()
         experiment._trial_indices_by_status = {status: set() for status in TrialStatus}
         trials = {
             obs["trial_index"]: Trial(
-                experiment, GeneratorRun(arms=[arms[obs["arm_name"]]])
+                experiment,
+                GeneratorRun(arms=[arms[assert_is_instance(obs["arm_name"], str)]]),
             )
             for obs in truth
         }
@@ -789,11 +772,19 @@ def test_ObservationsFromDataWithDifferentTimesSingleTrial(
             self.assertEqual(
                 obs.data.metric_signatures, obs_truth["metric_signatures"][i]
             )
-            # pyre-fixme[6]: For 2nd argument expected `Union[_SupportsArray[dtype[ty...
-            self.assertTrue(np.array_equal(obs.data.means, obs_truth["means"][i]))
             self.assertTrue(
-                # pyre-fixme[6]: For 2nd argument expected `Union[_SupportsArray[dtyp...
-                np.array_equal(obs.data.covariance, obs_truth["covariance"][i])
+                np.array_equal(
+                    obs.data.means,
+                    # pyre-ignore[6]: numpy stubs type mismatch.
+                    assert_is_instance(obs_truth["means"][i], np.ndarray),
+                )
+            )
+            self.assertTrue(
+                np.array_equal(
+                    obs.data.covariance,
+                    # pyre-ignore[6]: numpy stubs type mismatch.
+                    assert_is_instance(obs_truth["covariance"][i], np.ndarray),
+                )
             )
             self.assertEqual(obs.arm_name, obs_truth["arm_name"][i])
             self.assertEqual(obs.arm_name, obs_truth["arm_name"][i])
@@ -875,12 +866,10 @@ def test_ObservationsWithCandidateMetadata(self) -> None:
             },
         ]
         arms = {
-            # pyre-fixme[6]: For 1st param expected `Optional[str]` but got
-            #  `Union[Dict[str, Union[int, str]], float, str]`.
-            # pyre-fixme[6]: For 2nd param expected `Dict[str, Union[None, bool,
-            #  float, int, str]]` but got `Union[Dict[str, Union[int, str]], float,
-            #  str]`.
-            obs["arm_name"]: Arm(name=obs["arm_name"], parameters=obs["parameters"])
+            assert_is_instance(obs["arm_name"], str): Arm(
+                name=assert_is_instance(obs["arm_name"], str),
+                parameters=assert_is_instance(obs["parameters"], dict),
+            )
             for obs in truth
         }
         experiment = Mock()
@@ -889,9 +878,9 @@ def test_ObservationsWithCandidateMetadata(self) -> None:
             obs["trial_index"]: Trial(
                 experiment,
                 GeneratorRun(
-                    arms=[arms[obs["arm_name"]]],
+                    arms=[arms[assert_is_instance(obs["arm_name"], str)]],
                     candidate_metadata_by_arm_signature={
-                        arms[obs["arm_name"]].signature: {
+                        arms[assert_is_instance(obs["arm_name"], str)].signature: {
                             SOME_METADATA_KEY: f"value_{obs['trial_index']}"
                         }
                     },
@@ -919,8 +908,7 @@ def test_ObservationsWithCandidateMetadata(self) -> None:
         observations = observations_from_data(experiment, data)
         for observation in observations:
             self.assertEqual(
-                # pyre-fixme[16]: Optional type has no attribute `get`.
-                observation.features.metadata.get(SOME_METADATA_KEY),
+                none_throws(observation.features.metadata).get(SOME_METADATA_KEY),
                 f"value_{observation.features.trial_index}",
             )
 
diff --git a/ax/core/tests/test_outcome_constraint.py b/ax/core/tests/test_outcome_constraint.py
index fcc6ed79cf5..f288ece1703 100644
--- a/ax/core/tests/test_outcome_constraint.py
+++ b/ax/core/tests/test_outcome_constraint.py
@@ -253,8 +253,7 @@ def test_RaiseError(self) -> None:
             )
 
         with self.assertRaises(NotImplementedError):
-            # pyre-fixme[7]: Expected `None` but got `Metric`.
-            return self.constraint.metric
+            self.constraint.metric
 
         with self.assertRaises(NotImplementedError):
             self.constraint.metric = self.metrics[0]
diff --git a/ax/core/tests/test_parameter_constraint.py b/ax/core/tests/test_parameter_constraint.py
index 822046e1ec8..30bc1be4c2c 100644
--- a/ax/core/tests/test_parameter_constraint.py
+++ b/ax/core/tests/test_parameter_constraint.py
@@ -82,16 +82,12 @@ def test_Repr(self) -> None:
         self.assertEqual(str(self.constraint), self.constraint_repr)
 
     def test_Validate(self) -> None:
-        parameters = {"x": 4, "z": 3}
+        parameters: dict[str, float | int] = {"x": 4, "z": 3}
         with self.assertRaises(ValueError):
-            # pyre-fixme[6]: For 1st param expected `Dict[str, Union[float, int]]`
-            #  but got `Dict[str, int]`.
             self.constraint.check(parameters)
 
         # check slack constraint
         parameters = {"x": 4, "y": 1}
-        # pyre-fixme[6]: For 1st param expected `Dict[str, Union[float, int]]` but
-        #  got `Dict[str, int]`.
         self.assertTrue(self.constraint.check(parameters))
 
         # check tight constraint (within numerical tolerance)
diff --git a/ax/core/tests/test_runner.py b/ax/core/tests/test_runner.py
index 24edb5eb61e..40a34d33276 100644
--- a/ax/core/tests/test_runner.py
+++ b/ax/core/tests/test_runner.py
@@ -15,8 +15,7 @@
 
 
 class DummyRunner(Runner):
-    # pyre-fixme[3]: Return type must be annotated.
-    def run(self, trial: BaseTrial):
+    def run(self, trial: BaseTrial) -> dict[str, str]:
         return {"metadatum": f"value_for_trial_{trial.index}"}
 
 
diff --git a/ax/core/tests/test_trial.py b/ax/core/tests/test_trial.py
index 33eef439201..d638968896d 100644
--- a/ax/core/tests/test_trial.py
+++ b/ax/core/tests/test_trial.py
@@ -114,13 +114,12 @@ def test_basic_properties(self) -> None:
 
     def test_adding_new_trials(self) -> None:
         new_arm = get_arms()[1]
-        cand_metadata = {new_arm.signature: {"a": "b"}}
+        cand_metadata: dict[str, dict[str, str] | None] = {
+            new_arm.signature: {"a": "b"}
+        }
         new_trial = self.experiment.new_trial(
             generator_run=GeneratorRun(
                 arms=[new_arm],
-                # pyre-fixme[6]: For 2nd param expected `Optional[Dict[str,
-                #  Optional[Dict[str, typing.Any]]]]` but got `Dict[str, Dict[str,
-                #  str]]`.
                 candidate_metadata_by_arm_signature=cand_metadata,
             )
         )
@@ -313,15 +312,13 @@ def stop(self, trial, reason):
         f"{BaseTrial.__module__}.{BaseTrial.__name__}.lookup_data",
         return_value=TEST_DATA,
     )
-    # pyre-fixme[3]: Return type must be annotated.
-    def test_objective_mean(self, _mock):
+    def test_objective_mean(self, _mock: Mock) -> None:
         self.assertEqual(self.trial.objective_mean, 1.0)
 
     @patch(
         f"{BaseTrial.__module__}.{BaseTrial.__name__}.lookup_data", return_value=Data()
     )
-    # pyre-fixme[3]: Return type must be annotated.
-    def test_objective_mean_empty_df(self, _mock):
+    def test_objective_mean_empty_df(self, _mock: Mock) -> None:
         with self.assertRaisesRegex(ValueError, "not yet in data for trial."):
             self.assertIsNone(self.trial.objective_mean)
 
diff --git a/ax/core/tests/test_utils.py b/ax/core/tests/test_utils.py
index f0810e2949c..ccd64b6c4ec 100644
--- a/ax/core/tests/test_utils.py
+++ b/ax/core/tests/test_utils.py
@@ -22,6 +22,7 @@
 from ax.core.observation import ObservationFeatures
 from ax.core.optimization_config import OptimizationConfig
 from ax.core.outcome_constraint import OutcomeConstraint
+from ax.core.trial import Trial
 from ax.core.trial_status import TrialStatus
 from ax.core.types import ComparisonOp
 from ax.core.utils import (
@@ -51,7 +52,7 @@
     get_experiment,
     get_hierarchical_search_space_experiment,
 )
-from pyre_extensions import none_throws
+from pyre_extensions import assert_is_instance, none_throws
 
 
 class UtilsTest(TestCase):
@@ -1151,7 +1152,7 @@ def test_curve_data(self) -> None:
         )
         trial = exp.trials[0]
         trial.mark_running(no_runner_required=True)
-        arm_name = trial.arm.name  # pyre-ignore[16]
+        arm_name = none_throws(assert_is_instance(trial, Trial).arm).name
 
         # Both metrics present at various steps → COMPLETE.
         df_both = pd.DataFrame(
@@ -1183,7 +1184,7 @@ def test_curve_data(self) -> None:
         exp2.optimization_config = none_throws(exp.optimization_config)
         trial2 = exp2.trials[0]
         trial2.mark_running(no_runner_required=True)
-        arm_name2 = trial2.arm.name  # pyre-ignore[16]
+        arm_name2 = none_throws(assert_is_instance(trial2, Trial).arm).name
         df_partial = pd.DataFrame(
             [
                 {