diff --git a/src/lerobot/policies/vqbet/modeling_vqbet.py b/src/lerobot/policies/vqbet/modeling_vqbet.py
index 359b4fdb1d2..6d3976b7932 100644
--- a/src/lerobot/policies/vqbet/modeling_vqbet.py
+++ b/src/lerobot/policies/vqbet/modeling_vqbet.py
@@ -467,8 +467,8 @@ def discretize(self, n_vqvae_training_steps, actions):
         self.vqvae_model.optimized_steps += 1
         # if we updated RVQ more than `n_vqvae_training_steps` steps, we freeze the RVQ part.
         if self.vqvae_model.optimized_steps >= n_vqvae_training_steps:
-            self.vqvae_model.discretized = torch.tensor(True)
-            self.vqvae_model.vq_layer.freeze_codebook = torch.tensor(True)
+            self.vqvae_model.discretized.fill_(True)
+            self.vqvae_model.vq_layer.freeze_codebook.fill_(True)
             print("Finished discretizing action data!")
             self.vqvae_model.eval()
             for param in self.vqvae_model.vq_layer.parameters():
diff --git a/tests/policies/test_vqbet_modeling.py b/tests/policies/test_vqbet_modeling.py
new file mode 100644
index 00000000000..be40d982359
--- /dev/null
+++ b/tests/policies/test_vqbet_modeling.py
@@ -0,0 +1,164 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Regression tests for VQBeT modeling internals."""
+
+import pytest
+import torch
+
+from lerobot.configs.types import FeatureType, PolicyFeature
+from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig
+from lerobot.policies.vqbet.modeling_vqbet import VQBeTHead  # noqa: E402
+from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE
+
+
+def _make_minimal_config(n_vqvae_training_steps: int = 3) -> VQBeTConfig:
+    """Return a small VQBeTConfig suitable for CPU unit tests."""
+    config = VQBeTConfig()
+    config.input_features = {
+        OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(8,)),
+        OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 96, 96)),
+    }
+    config.output_features = {
+        ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(7,)),
+    }
+    # Use tiny sizes so the test is fast on CPU.
+    config.n_vqvae_training_steps = n_vqvae_training_steps
+    config.vqvae_n_embed = 8
+    config.vqvae_embedding_dim = 32
+    config.vqvae_enc_hidden_dim = 32
+    config.action_chunk_size = 2
+    config.crop_shape = (84, 84)
+    config.device = "cpu"
+    return config
+
+
+def test_discretize_updates_buffers_in_place():
+    """Regression test: discretize() must update registered buffers in-place.
+
+    Replacing them with `self.vqvae_model.discretized = torch.tensor(True)` creates
+    a new CPU tensor and breaks DDP buffer synchronisation with NCCL when the model
+    is on GPU (RuntimeError: No backend type associated with device type cpu).
+
+    This test verifies that after discretization the underlying tensor storage
+    (data_ptr) is unchanged, i.e. no new tensor was allocated.
+    """
+    config = _make_minimal_config(n_vqvae_training_steps=3)
+    head = VQBeTHead(config)
+    head.eval()
+
+    vqvae = head.vqvae_model
+    vq_layer = vqvae.vq_layer
+
+    # Capture storage addresses of the two registered buffers before discretization.
+    discretized_ptr_before = vqvae.discretized.data_ptr()
+    freeze_codebook_ptr_before = vq_layer.freeze_codebook.data_ptr()
+
+    # Both flags should be False at init.
+    assert not vqvae.discretized.item(), "discretized should be False before training"
+    assert not vq_layer.freeze_codebook.item(), "freeze_codebook should be False before training"
+
+    # Run discretize() until the threshold is crossed (n_vqvae_training_steps calls).
+    batch_size = 4
+    seq_len = config.action_chunk_size  # minimum sequence length that produces at least one chunk
+    action_dim = config.action_feature.shape[0]
+    dummy_actions = torch.randn(batch_size, seq_len, action_dim)
+
+    n_steps = config.n_vqvae_training_steps
+    for _ in range(n_steps):
+        head.discretize(n_steps, dummy_actions)
+
+    # After discretization both flags must be True.
+    assert vqvae.discretized.item(), "discretized should be True after training"
+    assert vq_layer.freeze_codebook.item(), "freeze_codebook should be True after training"
+
+    # Most importantly: the storage must be the *same* tensor objects (in-place update).
+    # If a new tensor was created the data_ptr would differ, and DDP would try to
+    # NCCL-broadcast a CPU tensor when the model lives on GPU.
+    assert vqvae.discretized.data_ptr() == discretized_ptr_before, (
+        "vqvae_model.discretized was replaced with a new tensor instead of being updated in-place. "
+        "This breaks DDP GPU buffer synchronisation."
+    )
+    assert vq_layer.freeze_codebook.data_ptr() == freeze_codebook_ptr_before, (
+        "vq_layer.freeze_codebook was replaced with a new tensor instead of being updated in-place. "
+        "This breaks DDP GPU buffer synchronisation."
+    )
+
+    # The buffers must still appear in the module's named_buffers() after update.
+    buffer_names = {name for name, _ in vqvae.named_buffers()}
+    assert "discretized" in buffer_names, (
+        "vqvae_model.discretized is no longer a registered buffer after discretize(). "
+        "Use .fill_() instead of direct assignment to preserve buffer registration."
+    )
+    freeze_buffer_names = {name for name, _ in vq_layer.named_buffers()}
+    assert "freeze_codebook" in freeze_buffer_names, (
+        "vq_layer.freeze_codebook is no longer a registered buffer after discretize(). "
+        "Use .fill_() instead of direct assignment to preserve buffer registration."
+    )
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_discretize_keeps_buffers_on_gpu():
+    """Regression test: discretize() must not move registered buffers from GPU to CPU.
+
+    This is the exact failure mode that caused the DDP crash:
+        RuntimeError: No backend type associated with device type cpu
+
+    When `self.vqvae_model.discretized = torch.tensor(True)` is used (wrong),
+    torch.tensor() creates a CPU tensor by default, silently moving the buffer
+    off the GPU. DDP's _sync_buffers() then tries to NCCL-broadcast a CPU tensor,
+    which NCCL does not support.
+
+    This test places the model on GPU and verifies that after discretize() both
+    buffers remain on CUDA, preventing the above regression.
+    """
+    config = _make_minimal_config(n_vqvae_training_steps=3)
+    head = VQBeTHead(config)
+    device = torch.device("cuda:0")
+    head = head.to(device)
+
+    vqvae = head.vqvae_model
+    vq_layer = vqvae.vq_layer
+
+    # Confirm buffers start on GPU.
+    assert vqvae.discretized.device.type == "cuda", "discretized should start on CUDA"
+    assert vq_layer.freeze_codebook.device.type == "cuda", "freeze_codebook should start on CUDA"
+
+    # Run discretize() until the threshold is crossed.
+    batch_size = 4
+    seq_len = config.action_chunk_size
+    action_dim = config.action_feature.shape[0]
+    dummy_actions = torch.randn(batch_size, seq_len, action_dim, device=device)
+
+    n_steps = config.n_vqvae_training_steps
+    for _ in range(n_steps):
+        head.discretize(n_steps, dummy_actions)
+
+    # Flags must be True.
+    assert vqvae.discretized.item(), "discretized should be True after training"
+    assert vq_layer.freeze_codebook.item(), "freeze_codebook should be True after training"
+
+    # Core assertion: buffers must still live on GPU after discretize().
+    # A direct-assignment `= torch.tensor(True)` creates a CPU tensor and fails here.
+    assert vqvae.discretized.device.type == "cuda", (
+        "vqvae_model.discretized was moved to CPU during discretize(). "
+        "This would cause 'RuntimeError: No backend type associated with device type cpu' "
+        "in DDP._sync_buffers(). Use .fill_(True) instead of = torch.tensor(True)."
+    )
+    assert vq_layer.freeze_codebook.device.type == "cuda", (
+        "vq_layer.freeze_codebook was moved to CPU during discretize(). "
+        "This would cause 'RuntimeError: No backend type associated with device type cpu' "
+        "in DDP._sync_buffers(). Use .fill_(True) instead of = torch.tensor(True)."
+    )