diff --git a/nemo_reinforcer/algorithms/sft.py b/nemo_reinforcer/algorithms/sft.py
index 0d06ad6366..5ff77e11e9 100644
--- a/nemo_reinforcer/algorithms/sft.py
+++ b/nemo_reinforcer/algorithms/sft.py
@@ -182,6 +182,7 @@ def setup(
         if last_checkpoint_path
         else None,
         init_optimizer=True,
+        init_reference_model=False,
     )
     loss_fn = NLLLoss()
     print(f"  ✓ Model initialized")
diff --git a/nemo_reinforcer/models/policy/hf_policy.py b/nemo_reinforcer/models/policy/hf_policy.py
index b2b004bddb..ebc9e879f2 100644
--- a/nemo_reinforcer/models/policy/hf_policy.py
+++ b/nemo_reinforcer/models/policy/hf_policy.py
@@ -68,6 +68,7 @@ def __init__(
         weights_path: Optional[str] = None,
         optimizer_path: Optional[str] = None,
         init_optimizer: bool = True,
+        init_reference_model: bool = True,
     ):
         self.cfg = config
         # torch distributed init. Envars for rank, world_size, and master_addr and master_port are set from the ray remote call
@@ -88,12 +89,14 @@ def __init__(
             device_map="cpu",  # load weights onto CPU initially
             torch_dtype=torch.float32,  # use full precision in sft until https://github.com/NVIDIA/reinforcer/issues/13 is fixed
         )
-        self.reference_model = AutoModelForCausalLM.from_pretrained(
-            model_name,
-            device_map="cpu",  # load weights onto CPU initially
-            torch_dtype=torch.float32,  # use full precision in sft until https://github.com/NVIDIA/reinforcer/issues/13 is fixed
-        )
-
+        if init_reference_model:
+            self.reference_model = AutoModelForCausalLM.from_pretrained(
+                model_name,
+                device_map="cpu",  # load weights onto CPU initially
+                torch_dtype=torch.float32,  # use full precision in sft until https://github.com/NVIDIA/reinforcer/issues/13 is fixed
+            )
+        else:
+            self.reference_model = None
         self.tokenizer = AutoTokenizer.from_pretrained(model_name)
         # If no pad token is defined, you might need:
         if self.tokenizer.pad_token is None:
@@ -123,9 +126,10 @@ def do_fsdp(model):
         self.model.to("cuda")
         self.model = do_fsdp(self.model)
         self.model = self.move_to_cpu(self.model)
-        self.reference_model.to("cuda")
-        self.reference_model = do_fsdp(self.reference_model)
-        self.reference_model = self.move_to_cpu(self.reference_model)
+        if self.reference_model is not None:
+            self.reference_model.to("cuda")
+            self.reference_model = do_fsdp(self.reference_model)
+            self.reference_model = self.move_to_cpu(self.reference_model)
         self.model.to("cuda")
         self._held_reference_model_params = None
         # register_fsdp_forward_method(self.model, "generate")
@@ -893,6 +897,7 @@ def __init__(
         init_optimizer: bool = True,
         weights_path: Optional[str] = None,
         optimizer_path: Optional[str] = None,
+        init_reference_model: bool = True,
     ):
         if weights_path:
             weights_path = os.path.abspath(weights_path)
@@ -905,6 +910,7 @@ def __init__(
             init_optimizer=init_optimizer,
             weights_path=weights_path,
             optimizer_path=optimizer_path,
+            init_reference_model=init_reference_model,
         )
         self.worker_group = RayWorkerGroup(
             cluster,
diff --git a/tests/unit/models/policy/test_hf_ray_policy.py b/tests/unit/models/policy/test_hf_ray_policy.py
index 29d8cbcbee..ded244feac 100644
--- a/tests/unit/models/policy/test_hf_ray_policy.py
+++ b/tests/unit/models/policy/test_hf_ray_policy.py
@@ -201,7 +201,7 @@ def training_setup():
         config = basic_llama_test_config
 
         print("Creating training HfPolicy...")
-        policy = HfPolicy(cluster=cluster, config=config)
+        policy = HfPolicy(cluster=cluster, config=config, init_reference_model=False)
 
         # Create a test batch
         print("Creating test batch...")
@@ -278,7 +278,7 @@ def verify_loss_tensor(loss_tensor):
 
 
 @pytest.fixture
-def generation_setup():
+def generation_setup(request):
     """Setup and teardown specifically for generation tests."""
     policy = None
     cluster = None
@@ -300,7 +300,9 @@ def generation_setup():
         config = basic_llama_test_config
 
         print("Creating generation HfPolicy...")
-        policy = HfPolicy(cluster=cluster, config=config)
+        policy = HfPolicy(
+            cluster=cluster, config=config, init_reference_model=request.param
+        )
 
         # Create a test batch
         print("Creating test batch...")
@@ -364,6 +366,7 @@ def generation_setup():
 
 
 @pytest.mark.timeout(180)
+@pytest.mark.parametrize("generation_setup", [False], indirect=True)
 def test_hf_policy_generation(generation_setup, tracker):
     policy, cluster, data, tokenizer, prompts, expected_generations = generation_setup
 
@@ -450,6 +453,7 @@ def test_hf_policy_generation(generation_setup, tracker):
 
 
 @pytest.mark.timeout(180)
+@pytest.mark.parametrize("generation_setup", [True], indirect=True)
 def test_all_hf_policy_generation_lps_ref_training(generation_setup):
     policy, cluster, data, tokenizer, prompts, expected_generations = generation_setup