diff --git a/nemo/collections/asr/parts/submodules/jasper.py b/nemo/collections/asr/parts/submodules/jasper.py
index ec0def1b3ebb..f29332ea522b 100644
--- a/nemo/collections/asr/parts/submodules/jasper.py
+++ b/nemo/collections/asr/parts/submodules/jasper.py
@@ -477,7 +477,13 @@ def forward_for_export(self, x, lengths):
             # Create sample mask - 1 represents value, 0 represents pad
             mask = self.make_pad_mask(lengths, max_audio_length=max_len, device=x.device)
             mask = ~mask  # 0 represents value, 1 represents pad
-            x = x.float()  # For stable AMP, SE must be computed at fp32.
+
+            # Ensure SE runs in FP32: cast fc weights and activations to float32
+            if self.fc[0].weight.dtype != torch.float32:
+                self.fc.float()
+            if x.dtype != torch.float32:
+                x = x.float()
+
             x = x.masked_fill(mask, 0.0)  # mask padded values explicitly to 0
             y = self._se_pool_step(x, mask)  # [B, C, 1]
             y = y.transpose(1, -1)  # [B, 1, C]
@@ -490,6 +496,8 @@ def forward_for_export(self, x, lengths):
 
             y = torch.sigmoid(y)
             y = x * y
+            # Cast back to original dtype for downstream consistency
+            y = y.to(dtype)
         return y, lengths
 
     def _se_pool_step(self, x, mask):
diff --git a/tutorials/llm/reasoning/Reasoning-SFT.ipynb b/tutorials/llm/reasoning/Reasoning-SFT.ipynb
index 78e67bbf2541..f0a2fd8dc609 100644
--- a/tutorials/llm/reasoning/Reasoning-SFT.ipynb
+++ b/tutorials/llm/reasoning/Reasoning-SFT.ipynb
@@ -32,7 +32,7 @@
     "* A valid Hugging Face API token with access to the [Meta LLaMa 3.1-8B Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) model (since this is a gated model).\n",
     "\n",
     "### Dataset\n",
-    "To follow along, you would need an appropriate reasoning dataset. Checkout the tutorial on [curating the Llama Nemotron Reasoning Dataset with NVIDIA NeMo Curator](https://github.com/NVIDIA/NeMo-Curator/tree/main/tutorials/llama-nemotron-data-curation).\n",
+    "To follow along, you would need an appropriate reasoning dataset. Checkout the tutorial on [curating the Llama Nemotron Reasoning Dataset with NVIDIA NeMo Curator](https://github.com/NVIDIA-NeMo/Curator/tree/dask/tutorials/llama-nemotron-data-curation).\n",
     "You will need the output from that tutorial as the training set input to this playbook!\n",
     "\n",
     "### Hardware Requirements\n",