diff --git a/src/mjlab/utils/buffers/circular_buffer.py b/src/mjlab/utils/buffers/circular_buffer.py
index f5daa7da9a..e2483159c2 100644
--- a/src/mjlab/utils/buffers/circular_buffer.py
+++ b/src/mjlab/utils/buffers/circular_buffer.py
@@ -208,10 +208,11 @@ def append(self, data: torch.Tensor) -> None:
     self._buffer[self._pointer] = data
 
     # Backfill entire history with first frame for newly initialized batches.
+    # The condition and data are reshaped to broadcast against the buffer's
+    # (max_len, batch_size, ...) shape for data of any rank.
     is_first_push = self._num_pushes == 0
-    torch.where(
-      is_first_push[None, :, None], data[None, :, :], self._buffer, out=self._buffer
-    )
+    condition = is_first_push.view(1, self._batch_size, *([1] * (data.ndim - 1)))
+    torch.where(condition, data.unsqueeze(0), self._buffer, out=self._buffer)
 
     self._num_pushes += 1
 
diff --git a/tests/test_circular_buffer.py b/tests/test_circular_buffer.py
index f571e9a112..34b9d02afb 100644
--- a/tests/test_circular_buffer.py
+++ b/tests/test_circular_buffer.py
@@ -161,6 +161,29 @@ def test_backfill_after_per_batch_reset(device):
   )
 
 
+def test_backfill_multidimensional_data(device):
+  """First-push backfill works for >2D data whose trailing dims differ from batch.
+
+  Regression for #1045: the backfill broadcast hard-coded a 3D condition shape,
+  which misaligned the batch axis with a trailing axis when num_targets != batch.
+  Here batch_size=4 but the trailing axis is 2, so the buggy broadcast raised a
+  4-vs-2 mismatch.
+  """
+  batch_size, num_targets, num_channels = 4, 2, 3
+  buffer = CircularBuffer(max_len=3, batch_size=batch_size, device=device)
+
+  data = torch.arange(
+    batch_size * num_targets * num_channels, dtype=torch.float32, device=device
+  ).reshape(batch_size, num_targets, num_channels)
+  buffer.append(data)
+
+  hist = buffer.buffer  # (batch, max_len, num_targets, num_channels)
+  assert hist.shape == (batch_size, 3, num_targets, num_channels)
+  # First push backfills every history slot with the same frame per batch row.
+  for t in range(3):
+    assert torch.allclose(hist[:, t], data)
+
+
 def test_errors_and_types(device):
   """Error paths: wrong batch, pre-append access, and bad key size."""
   buffer = CircularBuffer(max_len=2, batch_size=2, device=device)