diff --git a/nemo_automodel/components/distributed/cp_utils.py b/nemo_automodel/components/distributed/cp_utils.py
index c12ff9f39..d12641254 100644
--- a/nemo_automodel/components/distributed/cp_utils.py
+++ b/nemo_automodel/components/distributed/cp_utils.py
@@ -155,20 +155,25 @@ def _get_mesh_size(mesh):
     # CP doesn't support packed sequence currently. Let torch SDPA handle attention mask.
     batch.pop("attention_mask", None)
 
+    # Skip 1D injection if position_ids already in batch (e.g. mRoPE pre-computed)
     if "position_ids" not in batch and (_get_mesh_size(cp_mesh) > 1 or _get_mesh_size(tp_mesh) > 1):
         batch["position_ids"] = torch.arange(0, batch["input_ids"].shape[1]).unsqueeze(0).to(batch["input_ids"].device)
 
     input_ids = batch["input_ids"]
     position_ids = batch["position_ids"]
 
+    # Determine correct seq dim for CP sharding
+    # mRoPE: [3, B, S] → shard on dim 2; standard: [B, S] → shard on dim 1
+    pos_seq_dim = 2 if position_ids.ndim == 3 else 1
+
     labels = batch["labels"]
     if loss_mask is not None:
         cp_buffers = [input_ids, labels, position_ids, loss_mask]
-        cp_seq_dims = [1, 1, 1, 1]
+        cp_seq_dims = [1, 1, pos_seq_dim, 1]
         cp_no_restore_buffers = {input_ids, labels, loss_mask}
     else:
         cp_buffers = [input_ids, labels, position_ids]
-        cp_seq_dims = [1, 1, 1]
+        cp_seq_dims = [1, 1, pos_seq_dim]
         cp_no_restore_buffers = {input_ids, labels}
 
     cp_ctx = create_context_parallel_ctx(
diff --git a/tests/unit_tests/distributed/test_cp_utils.py b/tests/unit_tests/distributed/test_cp_utils.py
index b69f808ea..cc8866883 100644
--- a/tests/unit_tests/distributed/test_cp_utils.py
+++ b/tests/unit_tests/distributed/test_cp_utils.py
@@ -142,6 +142,93 @@ def _fake_get_train_ctx(enable_loss_parallel, enable_compiled_autograd, cp_ctx):
     assert new_batch is batch
 
 
+def test_make_cp_batch_and_ctx_3d_mrope_position_ids(monkeypatch):
+    """Verify that 3D mRoPE position_ids [3, B, S] are sharded on dim 2 (sequence), not dim 1 (batch)."""
+
+    captured_kwargs = {}
+
+    def _fake_create_ctx(**kwargs):
+        captured_kwargs.update(kwargs)
+        return object()
+
+    monkeypatch.setattr(_cu, "create_context_parallel_ctx", _fake_create_ctx)
+    monkeypatch.setattr(_cu, "get_train_context", lambda *_args, **_kw: "dummy_train_ctx")
+
+    device_mesh = _DummyDeviceMesh(cp_size=2, tp_size=1)
+    seq_len = 6
+    # mRoPE position_ids: [3, B, S] — temporal, height, width
+    position_ids_3d = torch.arange(3 * 1 * seq_len).view(3, 1, seq_len)
+    batch = {
+        "input_ids": torch.arange(seq_len).unsqueeze(0),
+        "labels": torch.arange(seq_len).unsqueeze(0),
+        "position_ids": position_ids_3d,
+    }
+
+    ctx_obj, new_batch = _cu.make_cp_batch_and_ctx(device_mesh, batch)
+
+    # position_ids should not have been overwritten (already present)
+    assert new_batch["position_ids"] is position_ids_3d
+
+    # The seq dims passed to create_context_parallel_ctx should shard position_ids on dim 2
+    assert "cp_seq_dims" in captured_kwargs
+    # input_ids dim=1, labels dim=1, position_ids dim=2
+    assert captured_kwargs["cp_seq_dims"] == [1, 1, 2]
+
+
+def test_make_cp_batch_and_ctx_2d_position_ids_seq_dim(monkeypatch):
+    """Verify that standard 2D position_ids [B, S] are still sharded on dim 1."""
+
+    captured_kwargs = {}
+
+    def _fake_create_ctx(**kwargs):
+        captured_kwargs.update(kwargs)
+        return object()
+
+    monkeypatch.setattr(_cu, "create_context_parallel_ctx", _fake_create_ctx)
+    monkeypatch.setattr(_cu, "get_train_context", lambda *_args, **_kw: "dummy_train_ctx")
+
+    device_mesh = _DummyDeviceMesh(cp_size=2, tp_size=1)
+    seq_len = 6
+    batch = {
+        "input_ids": torch.arange(seq_len).unsqueeze(0),
+        "labels": torch.arange(seq_len).unsqueeze(0),
+        "position_ids": torch.arange(seq_len).unsqueeze(0),
+    }
+
+    _cu.make_cp_batch_and_ctx(device_mesh, batch)
+
+    # Standard 2D: all seq dims should be 1
+    assert captured_kwargs["cp_seq_dims"] == [1, 1, 1]
+
+
+def test_make_cp_batch_and_ctx_3d_mrope_with_loss_mask(monkeypatch):
+    """Verify 3D mRoPE position_ids work correctly with loss_mask."""
+
+    captured_kwargs = {}
+
+    def _fake_create_ctx(**kwargs):
+        captured_kwargs.update(kwargs)
+        return object()
+
+    monkeypatch.setattr(_cu, "create_context_parallel_ctx", _fake_create_ctx)
+    monkeypatch.setattr(_cu, "get_train_context", lambda *_args, **_kw: "dummy_train_ctx")
+
+    device_mesh = _DummyDeviceMesh(cp_size=2, tp_size=1)
+    seq_len = 4
+    position_ids_3d = torch.arange(3 * 1 * seq_len).view(3, 1, seq_len)
+    loss_mask = torch.ones(1, seq_len)
+    batch = {
+        "input_ids": torch.arange(seq_len).unsqueeze(0),
+        "labels": torch.arange(seq_len).unsqueeze(0),
+        "position_ids": position_ids_3d,
+    }
+
+    _cu.make_cp_batch_and_ctx(device_mesh, batch, loss_mask=loss_mask)
+
+    # input_ids dim=1, labels dim=1, position_ids dim=2, loss_mask dim=1
+    assert captured_kwargs["cp_seq_dims"] == [1, 1, 2, 1]
+
+
 # ============================================================================
 # Tests for make_cp_batch_for_te
 # ============================================================================