diff --git a/megatron/core/distributed/distributed_data_parallel.py b/megatron/core/distributed/distributed_data_parallel.py
index 55179ff3024..7f3c0993bd8 100644
--- a/megatron/core/distributed/distributed_data_parallel.py
+++ b/megatron/core/distributed/distributed_data_parallel.py
@@ -490,10 +490,18 @@ def start_param_sync(self, *unused, force_sync: bool = False, force_dispatch: bo
                 # in "finish_param_sync" stage after zeroing the shared gardient buffers.
                 if self.ddp_config.reuse_grad_buf_for_mxfp8_param_ag:
                     for bucket in bucket_group.buckets:
+                        is_bf16_weight_bucket = False
                         for param in bucket.params:
+                            # Skip copying since bf16 weights in the mxfp8 model
+                            # are already mapped to param.data.
+                            if not is_float8tensor(param):
+                                is_bf16_weight_bucket = True
+                                break
                             param_start, param_end = bucket.param_to_index[param]
                             param_slice = bucket.param_data.view(-1)[param_start:param_end]
                             param.data.copy_(param_slice.view(param.data.shape))
+                        if is_bf16_weight_bucket:
+                            continue
                         # All-gathered params are not needed after being copied to param.data.
                         # Zero out the param buffer (shared with grad buffer) for gradient
                         # accumulation. We cannot zero out the entire grad buffer because one grad
diff --git a/megatron/core/distributed/param_and_grad_buffer.py b/megatron/core/distributed/param_and_grad_buffer.py
index 088374fbf13..9b74a8d7291 100644
--- a/megatron/core/distributed/param_and_grad_buffer.py
+++ b/megatron/core/distributed/param_and_grad_buffer.py
@@ -345,10 +345,18 @@ def finish_param_sync(self, skip_next_bucket_dispatch: bool = False):
             # after the param all-gather.
             if self.ddp_config.reuse_grad_buf_for_mxfp8_param_ag:
                 for bucket in self.buckets:
+                    is_bf16_weight_bucket = False
                     for param in bucket.params:
+                        # Skip copying since bf16 weights in the mxfp8 model
+                        # are already mapped to param.data.
+                        if not is_float8tensor(param):
+                            is_bf16_weight_bucket = True
+                            break
                         param_start, param_end = bucket.param_to_index[param]
                         param_slice = bucket.param_data.view(-1)[param_start:param_end]
                         param.data.copy_(param_slice.view(param.data.shape))
+                    if is_bf16_weight_bucket:
+                        continue
                     # All-gathered params are not needed after being copied to param.data.
                     # Zero out the param buffer (shared with grad buffer) for gradient accumulation.
                     # We cannot zero out the entire grad buffer because one grad buffer may
@@ -820,9 +828,9 @@ def _does_param_require_new_bucket(param):
         cur_bucket_id = 0
         for param in params[::-1]:
             param_start_index, param_end_index, bucket_id = self.param_index_map[param]
-            # For MXFP8 param: we only need to map weight gradients to the buffer.
-            if not self.ddp_config.reuse_grad_buf_for_mxfp8_param_ag:
-                # Assign param.data to appropriate segment of self.param_data.
+            # For MXFP8 param:
+            # we only need to map bf16 weights (layernorm, embedding, etc) to the buffer.
+            if not self.ddp_config.reuse_grad_buf_for_mxfp8_param_ag or not is_mxfp8tensor(param):
                 if self.param_data is not None:
                     new_param_data = self._get(
                         param.data.shape, param_start_index, buffer_type=BufferType.PARAM
diff --git a/tests/unit_tests/test_fp8_param.py b/tests/unit_tests/test_fp8_param.py
index 361698f7127..34b504e21de 100644
--- a/tests/unit_tests/test_fp8_param.py
+++ b/tests/unit_tests/test_fp8_param.py
@@ -11,7 +11,7 @@
 
 from megatron.core.distributed import DistributedDataParallel as DDP
 from megatron.core.enums import ModelType
-from megatron.core.fp8_utils import is_float8tensor
+from megatron.core.fp8_utils import is_float8tensor, is_mxfp8tensor
 from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
 from megatron.core.models.gpt.gpt_model import GPTModel
 from megatron.core.num_microbatches_calculator import destroy_num_microbatches_calculator
@@ -246,6 +246,28 @@ def _run_test_helper(
         if fp8_param_gather:
             assert num_fp8_params == 4 * fp8_layers
 
+        # Verify that bf16 params (embedding, LN, etc.) in the MXFP8 model are mapped
+        # to the param buffer (shared with grad buffer) rather than allocated separately.
+        if args.reuse_grad_buf_for_mxfp8_param_ag:
+            for buffer in gpt_model[0].buffers:
+                if buffer.param_data is None:
+                    continue
+                buf_start = buffer.param_data.data_ptr()
+                buf_end = buf_start + buffer.param_data.numel() * buffer.param_data.element_size()
+                for param in buffer.param_to_bucket:
+                    if is_mxfp8tensor(param):
+                        # MXFP8 params keep their own quantized storage.
+                        assert not (
+                            buf_start <= param.data.data_ptr() < buf_end
+                        ), "MXFP8 param should not be mapped to the param buffer"
+                    else:
+                        # BF16 params should be views into the param buffer
+                        # (no double allocation).
+                        assert buf_start <= param.data.data_ptr() < buf_end, (
+                            "BF16 param should be a view into the param buffer "
+                            "(no separate allocation)"
+                        )
+
         loss_list = []
 
         for i in range(100):