diff --git a/aepsych/strategy.py b/aepsych/strategy.py
index f3dc8f62b..d8f462d3f 100644
--- a/aepsych/strategy.py
+++ b/aepsych/strategy.py
@@ -410,9 +410,9 @@ def from_config(cls, config: Config, name: str) -> Strategy:
         stimuli_per_trial = config.getint(name, "stimuli_per_trial", fallback=1)
         outcome_types = config.getlist(name, "outcome_types", element_type=str)
 
-        generator = GeneratorWrapper.from_config(name, config)
+        generator = GeneratorWrapper.from_config(name, config, transforms)
 
-        model = ModelWrapper.from_config(name, config)
+        model = ModelWrapper.from_config(name, config, transforms)
 
         acqf_cls = config.getobj(name, "acqf", fallback=None)
         if acqf_cls is not None and hasattr(generator, "acqf"):
diff --git a/aepsych/transforms/parameters.py b/aepsych/transforms/parameters.py
index 685bb919d..e55aff07b 100644
--- a/aepsych/transforms/parameters.py
+++ b/aepsych/transforms/parameters.py
@@ -162,12 +162,14 @@ def from_config(
         cls,
         name: str,
         config: Config,
+        transforms: Optional[ChainedInputTransform] = None,
     ):
         gen_cls = config.getobj(name, "generator", fallback=SobolGenerator)
-        transforms = ParameterTransforms.from_config(config)
+        if transforms is None:
+            transforms = ParameterTransforms.from_config(config)
 
         # We need transformed values from config but we don't want to edit config
-        transformed_config = transform_options(config)
+        transformed_config = transform_options(config, transforms)
 
         gen = gen_cls.from_config(transformed_config)
 
@@ -281,27 +283,32 @@ def from_config(
         cls,
         name: str,
         config: Config,
+        transforms: Optional[ChainedInputTransform] = None,
     ):
         # We don't always have models
         model_cls = config.getobj(name, "model", fallback=None)
         if model_cls is None:
             return None
 
-        transforms = ParameterTransforms.from_config(config)
+        if transforms is None:
+            transforms = ParameterTransforms.from_config(config)
 
         # Need transformed values
-        transformed_config = transform_options(config)
+        transformed_config = transform_options(config, transforms)
 
         model = model_cls.from_config(transformed_config)
 
         return cls(model, transforms)
 
 
-def transform_options(config: Config) -> Config:
+def transform_options(
+    config: Config, transforms: Optional[ChainedInputTransform] = None
+) -> Config:
     """
     Return a copy of the config with the options transformed. The config
     """
-    transforms = ParameterTransforms.from_config(config)
+    if transforms is None:
+        transforms = ParameterTransforms.from_config(config)
 
     configClone = deepcopy(config)
 
diff --git a/tests/test_transforms.py b/tests/test_transforms.py
index 3514ce62f..134190117 100644
--- a/tests/test_transforms.py
+++ b/tests/test_transforms.py
@@ -100,6 +100,14 @@ def test_model_init_equivalent(self):
 
     def test_transforms_in_strategy(self):
         for _strat in self.strat.strat_list:
+            # Check if the same transform is passed around everywhere
+            self.assertTrue(id(_strat.transforms) == id(_strat.generator.transforms))
+            if _strat.model is not None:
+                self.assertTrue(
+                    id(_strat.generator.transforms) == id(_strat.model.transforms)
+                )
+
+            # Check all the transform bits are the same
             for strat_transform, gen_transform in zip(
                 _strat.transforms.items(), _strat.generator.transforms.items()
             ):