diff --git a/nemo_rl/data_plane/adapters/transfer_queue.py b/nemo_rl/data_plane/adapters/transfer_queue.py
index ce293b4e269..995cfa24c37 100644
--- a/nemo_rl/data_plane/adapters/transfer_queue.py
+++ b/nemo_rl/data_plane/adapters/transfer_queue.py
@@ -27,8 +27,9 @@
 import socket
 import subprocess
 import time
+import warnings
 from importlib import resources
-from typing import Any
+from typing import Any, cast
 
 import torch
 import transfer_queue as tq
@@ -39,6 +40,7 @@
     DataPlaneConfig,
     KVBatchMeta,
 )
+from nemo_rl.data_plane.schema import PROMOTE_1D_FIELDS
 
 # ──────────────────────────────────────────────────────────────────────────
 # Backend init — lifted from rl-arena/arena/backends.py.
@@ -171,27 +173,28 @@ def patched(*args, **kwargs):
         cls.options = patched  # type: ignore[method-assign]
         return True
 
-    patched_any = False
+    unpatched_classes: list[str] = []
     try:
-        from transfer_queue.storage.simple_backend import SimpleStorageUnit
+        from transfer_queue.storage.simple_storage import SimpleStorageUnit
 
-        patched_any |= _install(SimpleStorageUnit)
+        if not _install(SimpleStorageUnit):
+            unpatched_classes.append("SimpleStorageUnit")
     except ImportError:
-        pass
+        unpatched_classes.append("SimpleStorageUnit")
     try:
         from transfer_queue.controller import TransferQueueController
 
-        patched_any |= _install(TransferQueueController)
+        if not _install(TransferQueueController):
+            unpatched_classes.append("TransferQueueController")
     except ImportError:
-        pass
+        unpatched_classes.append("TransferQueueController")
 
-    if not patched_any:
+    if unpatched_classes:
         # Soft-fail: TQ may have moved its actor classes. The driver will
         # still work; multi-node TQ may need the per-node `uv sync` workaround.
-        import warnings
-
         warnings.warn(
-            "Could not patch TQ actor classes for runtime_env injection. "
+            "Could not patch every TQ actor class for runtime_env injection: "
+            f"unpatched={unpatched_classes}. "
             "Multi-node TQ may fail with ModuleNotFoundError: 'transfer_queue' "
             "on worker nodes. Workaround: run `uv sync` inside each node's "
             "container before the driver runs.",
@@ -322,19 +325,23 @@ def _assert_no_key_loss(src_dict: dict, new_td: TensorDict, fn: str) -> None:
 
 
 def _promote_1d_leaves(td: TensorDict) -> TensorDict:
-    """Unsqueeze 1D tensor leaves to ``(N, 1)`` — mooncake_cpu KV-path workaround.
+    """Promote declared scalar leaves to ``(N, 1)`` for Mooncake.
 
-    Works around TQ's ``KVStorageManager`` 1D schema/data mismatch;
-    :func:`_from_wire` squeezes the trailing 1 back on read. Symmetric
-    with `_from_wire` — callers gate on ``self._promote_1d``.
-    ``NonTensorStack`` / ``NonTensorData`` leaves pass through.
+    The authoritative field list lives in
+    :data:`nemo_rl.data_plane.schema.PROMOTE_1D_FIELDS`. Declared fields must
+    arrive as dense ``(N,)`` tensors. Any other dense 1D tensor is rejected so
+    it cannot silently encounter TQ v0.1.9's schema/data mismatch.
+    ``NonTensorStack`` and ``NonTensorData`` leaves pass through.
 
     Args:
-        td: ``TensorDict`` whose 1D tensor leaves should be promoted.
+        td: TensorDict to validate and encode for the Mooncake wire format.
 
     Returns:
-        ``TensorDict`` with 1D tensor leaves unsqueezed to ``(N, 1)``;
-        all other leaves pass through unchanged.
+        TensorDict with declared scalar leaves promoted to ``(N, 1)``.
+
+    Raises:
+        ValueError: If a declared field is not a dense 1D tensor, or an
+            undeclared field is a dense 1D tensor.
     """
     # td.keys() (top-level) includes NonTensorData / NonTensorStack leaves.
     # keys(include_nested=True, leaves_only=True) enumerates tensor leaves
@@ -343,9 +350,23 @@ def _promote_1d_leaves(td: TensorDict) -> TensorDict:
     changed = False
     for k in td.keys():
         v = td.get(k)
-        if isinstance(v, torch.Tensor) and not v.is_nested and v.dim() == 1:
+        field_name = str(k)
+        if field_name in PROMOTE_1D_FIELDS:
+            if not isinstance(v, torch.Tensor) or v.is_nested or v.dim() != 1:
+                shape = tuple(v.shape) if isinstance(v, torch.Tensor) else None
+                raise ValueError(
+                    f"Mooncake scalar field {field_name!r} must be a dense "
+                    f"1D tensor with shape (N,), got {type(v).__name__} "
+                    f"with shape {shape}."
+                )
             new_dict[str(k)] = v.unsqueeze(-1).contiguous()
             changed = True
+        elif isinstance(v, torch.Tensor) and not v.is_nested and v.dim() == 1:
+            raise ValueError(
+                f"Mooncake field {field_name!r} is a dense 1D tensor but is "
+                "not declared in data_plane.schema.PROMOTE_1D_FIELDS. Add "
+                "the field to the schema if it is a per-sample scalar."
+            )
         else:
             new_dict[str(k)] = v
     if not changed:
@@ -356,23 +377,45 @@ def _promote_1d_leaves(td: TensorDict) -> TensorDict:
 
 
 def _from_wire(td: TensorDict) -> TensorDict:
-    """Inverse of `_promote_1d_leaves`: squeeze trailing 1 back to (N,)."""
+    """Normalize TQ reads and invert :func:`_promote_1d_leaves` when needed.
+
+    Both TQ v0.1.9 storage managers reconstruct every non-scalar field as a
+    nested tensor, including fields whose rows all have the same shape.
+    Densify those uniform nested tensors first so regular batched inputs retain
+    their dense representation. Truly ragged fields remain nested. Finally,
+    squeeze only singleton dimensions declared in
+    :data:`nemo_rl.data_plane.schema.PROMOTE_1D_FIELDS`.
+    """
     # Same top-level iteration as `_promote_1d_leaves`: NonTensorData /
     # NonTensorStack leaves are only visible via td.keys(), not leaves_only.
     new_dict: dict[str, Any] = {}
     changed = False
     for k in td.keys():
         v = td.get(k)
-        if (
-            isinstance(v, torch.Tensor)
-            and not v.is_nested
-            and v.dim() >= 2
-            and v.shape[-1] == 1
-        ):
-            new_dict[str(k)] = v.squeeze(-1).contiguous()
-            changed = True
+        field_name = str(k)
+        if isinstance(v, torch.Tensor) and v.is_nested:
+            rows = list(v.unbind())
+            if rows and all(row.shape == rows[0].shape for row in rows[1:]):
+                v = torch.stack(rows)
+                changed = True
+        if field_name in PROMOTE_1D_FIELDS:
+            if not isinstance(v, torch.Tensor) or v.is_nested:
+                raise ValueError(
+                    f"Mooncake scalar field {field_name!r} could not be "
+                    "restored as a dense tensor."
+                )
+            if v.dim() == 1:
+                new_dict[field_name] = v
+            elif v.dim() == 2 and v.shape[-1] == 1:
+                new_dict[field_name] = v.squeeze(-1).contiguous()
+                changed = True
+            else:
+                raise ValueError(
+                    f"Mooncake scalar field {field_name!r} must decode as "
+                    f"(N,) or (N, 1), got shape {tuple(v.shape)}."
+                )
         else:
-            new_dict[str(k)] = v
+            new_dict[field_name] = v
     if not changed:
         return td
     new_td = TensorDict(new_dict, batch_size=td.batch_size)
@@ -583,9 +626,9 @@ def put_samples(
             return KVBatchMeta(
                 partition_id=partition_id, task_name=None, sample_ids=[], fields=None
             )
-        if tags is None:
-            tags = [{} for _ in sample_ids]
-
+        user_tags = (
+            [{} for _ in sample_ids] if tags is None else [dict(tag) for tag in tags]
+        )
         wire_fields: TensorDict | None = None
         field_names: list[str] | None = None
         if fields is not None:
@@ -594,17 +637,21 @@ def put_samples(
             # TDs. TQ's encoder forces ``.contiguous()`` per tensor leaf
             # itself, so the call here was redundant for tensors and
             # destructive for non-tensors.
-            wire_fields = fields.detach()  # type: ignore[bad-assignment,missing-argument]
+            detached_fields = cast(
+                TensorDict,
+                fields.detach(),  # type: ignore[missing-argument]
+            )
             if self._promote_1d:
-                wire_fields = _promote_1d_leaves(wire_fields)  # type: ignore[bad-argument-type]
-            field_names = list(wire_fields.keys())
+                detached_fields = _promote_1d_leaves(detached_fields)
+            wire_fields = detached_fields
+            field_names = [str(key) for key in detached_fields.keys()]
 
         # TQ's wire vocabulary is `keys=` — translation point.
         tq.kv_batch_put(
             keys=list(sample_ids),
             partition_id=partition_id,
             fields=wire_fields,
-            tags=tags,
+            tags=user_tags,
         )
 
         return KVBatchMeta(
@@ -612,7 +659,7 @@ def put_samples(
             task_name=None,
             sample_ids=list(sample_ids),
             fields=field_names,
-            tags=[dict(t) for t in tags] if tags else None,
+            tags=user_tags if user_tags else None,
         )
 
     def get_samples(
@@ -623,15 +670,12 @@ def get_samples(
     ) -> TensorDict:
         if not sample_ids:
             return TensorDict({}, batch_size=(0,))
-        # TQ's wire vocabulary is `keys=` — translation point.
         td = tq.kv_batch_get(
             keys=list(sample_ids),
             partition_id=partition_id,
             select_fields=select_fields,
         )
-        if self._promote_1d:
-            td = _from_wire(td)
-        return td
+        return _from_wire(td)
 
     def clear_samples(self, sample_ids: list[str] | None, partition_id: str) -> None:
         cleared_via_none = sample_ids is None
diff --git a/nemo_rl/data_plane/schema.py b/nemo_rl/data_plane/schema.py
index e451fc361cc..49cf79422e7 100644
--- a/nemo_rl/data_plane/schema.py
+++ b/nemo_rl/data_plane/schema.py
@@ -63,6 +63,25 @@
 
 ROUTED_EXPERTS_FIELD = "routed_experts"
 
+# Per-sample 1D scalar fields. The TQ adapter promotes these to ``(N, 1)``
+# on write to work around TQ v0.1.9's KVStorageManager schema/data mismatch on
+# the Mooncake backend, and squeezes them back to ``(N,)`` on read. This is the
+# authoritative user-level schema; no per-row shape metadata is carried.
+#
+# Fields listed here must be dense ``(N,)`` tensors when written through the
+# Mooncake adapter. Dense 1D fields not listed here are rejected on that path so
+# a new field cannot silently reintroduce the upstream shape mismatch.
+#
+# Delete this set and the corresponding adapter transforms when upstream TQ
+# fixes 1D field schema extraction.
+PROMOTE_1D_FIELDS: frozenset[str] = frozenset(
+    {
+        INPUT_LENGTHS,
+        "total_reward",
+        SAMPLE_MASK,
+    }
+)
+
 
 def fields_with_optional_routed_experts(
     fields: Sequence[str],
diff --git a/pyproject.toml b/pyproject.toml
index 9958048b578..1b4244c5727 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -84,11 +84,10 @@ dependencies = [
   # automatically include them. Removes the need for a `[data-plane]`
   # extra and the corresponding plumbing in the per-worker venv builder.
   "tensordict",
-  # Pinned to b266d39 (post-0.1.6, pre-0.1.7) for PR #77's MooncakeStore
-  # refactor: `clear` switched from unanchored `remove_by_regex` to
-  # exact-key `batch_remove`, which fixes a collateral-key-deletion bug
-  # that breaks DAPO + mooncake_cpu. Bump to the 0.1.7 tag when released.
-  "TransferQueue @ git+https://github.com/Ascend/TransferQueue.git@b266d39",
+  # TransferQueue v0.1.9 adds full-system checkpoint save/load APIs and
+  # retains the exact-key MooncakeStore clear behavior required by DAPO.
+  # Pin the immutable release commit rather than the mutable version tag.
+  "TransferQueue @ git+https://github.com/Ascend/TransferQueue.git@c51614308b68c8d7a87c9b3ef62d59e14c69bde2",
   # Backs data_plane.backend="mooncake_cpu". Default backend is "simple"
   # (in-process), but the mooncake_cpu path needs the `mooncake_master`
   # binary that ships in this wheel at <site-packages>/mooncake/. Bundled
@@ -391,10 +390,9 @@ override-dependencies = [
   "pytest>=9.0.3",
   "langchain>=0.3.28",      # Address CVE-2025-65106
   "langchain-core>=0.3.80", # Address CVE-2025-65106
-  # TransferQueue (data-plane extra) pins numpy<2.0.0; megatron-core needs
-  # numpy>=2.1.0 via onnx → ml-dtypes. Override globally so the data-plane
-  # extra composes with mcore/automodel without version-mirroring TQ's
-  # requirements.txt. Forward-compatible across TQ minor bumps.
+  # Forces numpy past tensorrt-llm's `numpy>=2.0.0,<2.4` cap (resolves to
+  # 2.5.x). The original driver — TransferQueue pinning `numpy<2.0.0` — is
+  # gone as of TQ v0.1.9; drop this override once the trtllm cap lifts.
   "numpy>=2.1.0",
   # av (PyAV) carries CVE-bundled codec libs (libx264, libx265, libopenh264, libmp3lame).
   # It is only needed by megatron-bridge's optional WAN diffusion path, which installs it
diff --git a/tests/unit/data_plane/README.md b/tests/unit/data_plane/README.md
index f37b6b5852a..9ef9f60d417 100644
--- a/tests/unit/data_plane/README.md
+++ b/tests/unit/data_plane/README.md
@@ -30,10 +30,14 @@ Generated audit of every test function under `tests/unit/data_plane/` with a one
 - `test_materialize_default_pad_value_is_zero` — No `pad_value_dict` → pad with 0.
 - `test_response_from_nested_extracts_response_slice` — Worker write-back: jagged (prompt+response) → response only.
 
-## `test_codec_mooncake.py` (4 tests)
-
-- `test_promote_1d_leaves_unsqueezes_1d` — `_promote_1d_leaves` turns 1D `(N,)` leaves into `(N, 1)` for mooncake wire.
-- `test_promote_1d_roundtrip_via_from_wire` — `_promote_1d_leaves` + `_from_wire` restores original `(N,)` shape and values.
+## `test_codec_mooncake.py`
+
+- `test_promote_1d_leaves_unsqueezes_1d` — `_promote_1d_leaves` turns schema-declared scalar fields from `(N,)` into `(N, 1)` for the Mooncake wire.
+- `test_promote_1d_roundtrip_via_from_wire` — `_promote_1d_leaves` + `_from_wire` restores the schema-declared field's original `(N,)` shape and values.
+- `test_from_wire_rejects_invalid_declared_field_shape` — Corrupt or incompatible scalar wire shapes fail at the data-plane boundary.
+- `test_promote_1d_leaves_rejects_undeclared_1d_field` — Unknown dense 1D Mooncake fields fail loudly instead of silently hitting TQ's shape mismatch.
+- `test_put_samples_uses_schema_without_private_shape_tags` — Promotion does not add per-row adapter metadata to user tags.
+- `test_get_samples_uses_static_shape_schema` — Reads restore scalar fields by the shared schema while preserving genuine `(N, 1)` columns.
 - `test_pack_per_token_field_truncates_sp_padding` — pack_per_token_field slices each row to its own length, dropping SP padding.
 - `test_pack_per_token_field_exact_fit_matches_to_nested_by_length` — At exact fit, `pack_per_token_field` matches `to_nested_by_length`.
 
diff --git a/tests/unit/data_plane/test_codec_mooncake.py b/tests/unit/data_plane/test_codec_mooncake.py
index c9b71820d74..68752b5bb58 100644
--- a/tests/unit/data_plane/test_codec_mooncake.py
+++ b/tests/unit/data_plane/test_codec_mooncake.py
@@ -14,7 +14,7 @@
 """Unit tests for the mooncake_cpu-specific wire workarounds.
 
 Covers:
-  P1 — `promote_1d` round-trip: writer unsqueezes 1D → (N,1), reader squeezes back.
+  P1 — schema-declared 1D scalar round-trip through the Mooncake workaround.
   P2 — pack_per_token_field: tolerates SP padding wider than max(lengths).
 
 No Ray, no GPU, no transfer_queue required.
@@ -22,6 +22,7 @@
 
 from __future__ import annotations
 
+import pytest
 import torch
 
 from nemo_rl.data_plane.codec import pack_per_token_field, to_nested_by_length
@@ -44,11 +45,12 @@ def test_promote_1d_leaves_unsqueezes_1d() -> None:
 
     n = 8
     t = torch.arange(n, dtype=torch.float32)
-    td = TensorDict({"reward": t}, batch_size=[n])
+    td = TensorDict({"input_lengths": t}, batch_size=[n])
 
     out = _promote_1d_leaves(td)
-    assert out["reward"].shape == (n, 1), (
-        f"Expected wire shape ({n}, 1) but got {tuple(out['reward'].shape)}."
+    assert out["input_lengths"].shape == (n, 1), (
+        "Expected input_lengths to use the Mooncake wire shape "
+        f"({n}, 1), got {tuple(out['input_lengths'].shape)}."
     )
 
 
@@ -63,14 +65,234 @@ def test_promote_1d_roundtrip_via_from_wire() -> None:
 
     n = 6
     original = torch.arange(n, dtype=torch.float32)
-    td = TensorDict({"reward": original}, batch_size=[n])
+    td = TensorDict({"input_lengths": original}, batch_size=[n])
 
     wire = _promote_1d_leaves(td)
-    assert wire["reward"].shape == (n, 1)
+    assert wire["input_lengths"].shape == (n, 1)
 
     back = _from_wire(wire)
-    assert back["reward"].shape == (n,)
-    assert torch.equal(back["reward"], original)
+    assert back["input_lengths"].shape == (n,)
+    assert torch.equal(back["input_lengths"], original)
+
+
+def test_from_wire_densifies_uniform_nested_rows() -> None:
+    """TQ v0.1.9's uniform nested reads are restored to dense tensors."""
+    from tensordict import TensorDict
+
+    from nemo_rl.data_plane.adapters.transfer_queue import _from_wire
+
+    rows = [torch.tensor([i, i + 1], dtype=torch.float32) for i in range(4)]
+    wire = TensorDict(
+        {"input_ids": torch.nested.as_nested_tensor(rows, layout=torch.jagged)},
+        batch_size=[len(rows)],
+    )
+
+    back = _from_wire(wire)
+
+    assert not back["input_ids"].is_nested
+    assert back["input_ids"].shape == (len(rows), 2)
+    assert torch.equal(back["input_ids"], torch.stack(rows))
+
+
+def test_from_wire_preserves_genuine_length_one_token_column() -> None:
+    """Only fields promoted from ``(N,)`` are squeezed after a TQ read."""
+    from tensordict import TensorDict
+
+    from nemo_rl.data_plane.adapters.transfer_queue import _from_wire
+
+    n = 4
+    wire = TensorDict(
+        {
+            "total_reward": torch.nested.as_nested_tensor(
+                [torch.tensor([float(i)]) for i in range(n)], layout=torch.jagged
+            ),
+            "input_ids": torch.nested.as_nested_tensor(
+                [torch.tensor([i]) for i in range(n)], layout=torch.jagged
+            ),
+        },
+        batch_size=[n],
+    )
+
+    back = _from_wire(wire)
+
+    assert back["total_reward"].shape == (n,)
+    assert back["input_ids"].shape == (n, 1)
+    assert torch.equal(back["input_ids"], torch.arange(n).unsqueeze(-1))
+
+
+def test_from_wire_rejects_invalid_declared_field_shape() -> None:
+    """A corrupted scalar wire shape fails at the data-plane boundary."""
+    from tensordict import TensorDict
+
+    from nemo_rl.data_plane.adapters.transfer_queue import _from_wire
+
+    wire = TensorDict({"input_lengths": torch.ones(3, 2)}, batch_size=[3])
+
+    with pytest.raises(ValueError, match=r"input_lengths.*\(N, 1\)"):
+        _from_wire(wire)
+
+
+def test_promote_1d_leaves_rejects_undeclared_1d_field() -> None:
+    """New scalar fields must be added to the authoritative schema."""
+    from tensordict import TensorDict
+
+    from nemo_rl.data_plane.adapters.transfer_queue import _promote_1d_leaves
+
+    fields = TensorDict({"new_scalar": torch.arange(3)}, batch_size=[3])
+
+    with pytest.raises(ValueError, match="not declared.*PROMOTE_1D_FIELDS"):
+        _promote_1d_leaves(fields)
+
+
+def test_promote_1d_leaves_rejects_invalid_declared_field_shape() -> None:
+    """A schema-declared scalar cannot silently change its user-level rank."""
+    from tensordict import TensorDict
+
+    from nemo_rl.data_plane.adapters.transfer_queue import _promote_1d_leaves
+
+    fields = TensorDict({"input_lengths": torch.ones(3, 2)}, batch_size=[3])
+
+    with pytest.raises(ValueError, match=r"input_lengths.*shape \(N,\)"):
+        _promote_1d_leaves(fields)
+
+
+def test_put_samples_uses_schema_without_private_shape_tags(monkeypatch) -> None:
+    """Mooncake promotion changes tensors but not user-provided TQ tags."""
+    from tensordict import TensorDict
+
+    import nemo_rl.data_plane.adapters.transfer_queue as tq_adapter
+
+    n = 3
+    original_fields = TensorDict(
+        {
+            "input_lengths": torch.arange(n),
+            "input_ids": torch.arange(n).unsqueeze(-1),
+        },
+        batch_size=[n],
+    )
+    user_tags = [{"weight_version": 7} for _ in range(n)]
+
+    def fake_kv_batch_put(
+        *,
+        keys: list[str],
+        partition_id: str,
+        fields: TensorDict,
+        tags: list[dict[str, object]],
+    ) -> None:
+        assert keys == ["a", "b", "c"]
+        assert partition_id == "train"
+        assert fields["input_lengths"].shape == (n, 1)
+        assert fields["input_ids"].shape == (n, 1)
+        assert tags == user_tags
+
+    monkeypatch.setattr(tq_adapter.tq, "kv_batch_put", fake_kv_batch_put)
+    client = object.__new__(tq_adapter.TQDataPlaneClient)
+    client._promote_1d = True
+
+    meta = client.put_samples(
+        ["a", "b", "c"], "train", fields=original_fields, tags=user_tags
+    )
+
+    assert meta.tags == user_tags
+
+
+def test_get_samples_uses_static_shape_schema(monkeypatch) -> None:
+    """The Mooncake adapter restores scalar ranks without row metadata."""
+    from tensordict import TensorDict
+
+    import nemo_rl.data_plane.adapters.transfer_queue as tq_adapter
+
+    n = 3
+    original = TensorDict(
+        {
+            "total_reward": torch.arange(n, dtype=torch.float32),
+            "input_ids": torch.arange(n).unsqueeze(-1),
+        },
+        batch_size=[n],
+    )
+    wire_data = TensorDict(
+        {
+            "total_reward": torch.nested.as_nested_tensor(
+                [row for row in original["total_reward"].unsqueeze(-1)],
+                layout=torch.jagged,
+            ),
+            "input_ids": torch.nested.as_nested_tensor(
+                [row for row in original["input_ids"]], layout=torch.jagged
+            ),
+        },
+        batch_size=[n],
+    )
+
+    def fake_kv_batch_get(
+        *, keys: list[str], partition_id: str, select_fields: list[str]
+    ) -> TensorDict:
+        assert keys == ["a", "b", "c"]
+        assert partition_id == "train"
+        assert select_fields == ["total_reward", "input_ids"]
+        return wire_data
+
+    monkeypatch.setattr(tq_adapter.tq, "kv_batch_get", fake_kv_batch_get)
+    client = object.__new__(tq_adapter.TQDataPlaneClient)
+    client._promote_1d = True
+
+    restored = client.get_samples(
+        ["a", "b", "c"], "train", ["total_reward", "input_ids"]
+    )
+
+    assert restored["total_reward"].shape == (n,)
+    assert restored["input_ids"].shape == (n, 1)
+    assert torch.equal(restored["total_reward"], original["total_reward"])
+    assert torch.equal(restored["input_ids"], original["input_ids"])
+
+
+def test_get_samples_densifies_uniform_rows_without_1d_promotion(monkeypatch) -> None:
+    """The simple backend normalizes uniform nested rows without squeezing."""
+    from tensordict import TensorDict
+
+    import nemo_rl.data_plane.adapters.transfer_queue as tq_adapter
+
+    rows = [torch.tensor([1, 2]), torch.tensor([3, 4])]
+    wire_data = TensorDict(
+        {"input_ids": torch.nested.as_nested_tensor(rows, layout=torch.jagged)},
+        batch_size=[len(rows)],
+    )
+
+    def fake_kv_batch_get(
+        *, keys: list[str], partition_id: str, select_fields: list[str]
+    ) -> TensorDict:
+        assert keys == ["a", "b"]
+        assert partition_id == "train"
+        assert select_fields == ["input_ids"]
+        return wire_data
+
+    monkeypatch.setattr(tq_adapter.tq, "kv_batch_get", fake_kv_batch_get, raising=False)
+    client = object.__new__(tq_adapter.TQDataPlaneClient)
+    client._promote_1d = False
+
+    restored = client.get_samples(["a", "b"], "train", ["input_ids"])
+
+    assert not restored["input_ids"].is_nested
+    assert restored["input_ids"].shape == (2, 2)
+    assert torch.equal(restored["input_ids"], torch.stack(rows))
+
+
+def test_from_wire_preserves_ragged_nested_rows() -> None:
+    """Variable-length rollout fields must remain nested."""
+    from tensordict import TensorDict
+
+    from nemo_rl.data_plane.adapters.transfer_queue import _from_wire
+
+    rows = [torch.arange(i + 1) for i in range(3)]
+    nested = torch.nested.as_nested_tensor(rows, layout=torch.jagged)
+    wire = TensorDict({"token_ids": nested}, batch_size=[len(rows)])
+
+    back = _from_wire(wire)
+
+    assert back["token_ids"].is_nested
+    assert all(
+        torch.equal(actual, expected)
+        for actual, expected in zip(back["token_ids"].unbind(), rows, strict=True)
+    )
 
 
 # ── P2: pack_per_token_field — tolerates SP padding ──────────────────────────
diff --git a/tests/unit/data_plane/test_tq_lifecycle.py b/tests/unit/data_plane/test_tq_lifecycle.py
index a349c27c2e7..3e79a50ff84 100644
--- a/tests/unit/data_plane/test_tq_lifecycle.py
+++ b/tests/unit/data_plane/test_tq_lifecycle.py
@@ -134,10 +134,11 @@ def test_smoke_round_trip_backends(tq_client_backends) -> None:
         consumer_tasks=["read"],
     )
     keys = ["a", "b", "c", "d"]
+    values = torch.arange(12).reshape(4, 3)
     client.put_samples(
         sample_ids=keys,
         partition_id="smoke-backend",
-        fields=TensorDict({"x": torch.arange(4)}, batch_size=[4]),
+        fields=TensorDict({"x": values}, batch_size=[4]),
     )
 
     meta = client.claim_meta(
@@ -150,7 +151,9 @@ def test_smoke_round_trip_backends(tq_client_backends) -> None:
     assert meta.size == 4
 
     data = client.get_data(meta)
-    expected = torch.tensor([keys.index(k) for k in meta.sample_ids])
+    expected = torch.stack([values[keys.index(k)] for k in meta.sample_ids])
+    assert not data["x"].is_nested
+    assert data["x"].shape == expected.shape
     assert torch.equal(data["x"], expected)
 
     client.clear_samples(sample_ids=None, partition_id="smoke-backend")
@@ -164,11 +167,11 @@ def test_smoke_round_trip_1d_fields(tq_client_backends) -> None:
     this for mooncake_cpu; simple passes the tensor through unchanged.
     """
     n = 6
-    reward = torch.arange(n, dtype=torch.float32)
+    total_reward = torch.arange(n, dtype=torch.float32)
 
     tq_client_backends.register_partition(
         partition_id="smoke-1d",
-        fields=["reward"],
+        fields=["total_reward"],
         num_samples=n,
         consumer_tasks=["read"],
     )
@@ -176,21 +179,21 @@ def test_smoke_round_trip_1d_fields(tq_client_backends) -> None:
     tq_client_backends.put_samples(
         sample_ids=keys,
         partition_id="smoke-1d",
-        fields=TensorDict({"reward": reward}, batch_size=[n]),
+        fields=TensorDict({"total_reward": total_reward}, batch_size=[n]),
     )
 
     meta = tq_client_backends.claim_meta(
         partition_id="smoke-1d",
         task_name="read",
-        required_fields=["reward"],
+        required_fields=["total_reward"],
         batch_size=n,
         timeout_s=30.0,
     )
     data = tq_client_backends.get_data(meta)
 
-    assert data["reward"].shape == reward.shape, (
-        f"Expected shape {tuple(reward.shape)} for 1D field, "
-        f"got {tuple(data['reward'].shape)}. "
+    assert data["total_reward"].shape == total_reward.shape, (
+        f"Expected shape {tuple(total_reward.shape)} for 1D field, "
+        f"got {tuple(data['total_reward'].shape)}. "
         "TQ must not unsqueeze 1D tensors silently (R-C2)."
     )
 
diff --git a/uv.lock b/uv.lock
index 940c29c2ed9..ac5225635a8 100644
--- a/uv.lock
+++ b/uv.lock
@@ -4467,7 +4467,7 @@ requires-dist = [
     { name = "torchdata" },
     { name = "torchvision", marker = "sys_platform != 'darwin'", specifier = "==0.26.0", index = "https://download.pytorch.org/whl/cu130" },
     { name = "torchvision", marker = "sys_platform == 'darwin'", specifier = "==0.26.0", index = "https://pypi.org/simple" },
-    { name = "transferqueue", git = "https://github.com/Ascend/TransferQueue.git?rev=b266d39" },
+    { name = "transferqueue", git = "https://github.com/Ascend/TransferQueue.git?rev=c51614308b68c8d7a87c9b3ef62d59e14c69bde2" },
     { name = "transformer-engine", extras = ["core-cu13", "pytorch"], marker = "extra == 'automodel'", git = "https://github.com/NVIDIA/TransformerEngine.git?rev=v2.14.1" },
     { name = "transformers", specifier = ">=5.5.0,<5.9.0" },
     { name = "transformers", marker = "extra == 'automodel'", specifier = ">=5.5.0,<5.6.0" },
@@ -8004,14 +8004,15 @@ wheels = [
 
 [[package]]
 name = "transferqueue"
-version = "0.1.7.dev0"
-source = { git = "https://github.com/Ascend/TransferQueue.git?rev=b266d39#b266d39a15aae114730de36cf8317b6285436f7f" }
+version = "0.1.9"
+source = { git = "https://github.com/Ascend/TransferQueue.git?rev=c51614308b68c8d7a87c9b3ef62d59e14c69bde2#c51614308b68c8d7a87c9b3ef62d59e14c69bde2" }
 dependencies = [
     { name = "hydra-core", version = "1.3.2", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-trtllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-trtllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-trtllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-trtllm' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-7-nemo-rl-mcore') or (platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-nemo-rl-mcore') or (platform_machine == 'aarch64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (platform_machine == 'aarch64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (platform_machine == 'aarch64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine == 'aarch64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-trtllm') or (platform_machine == 'aarch64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine == 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (platform_machine == 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (platform_machine == 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine == 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-trtllm') or (platform_machine == 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine == 'aarch64' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine == 'aarch64' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-trtllm') or (platform_machine == 'aarch64' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine == 'aarch64' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-trtllm' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine == 'x86_64' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine == 'x86_64' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-trtllm') or (platform_machine == 'x86_64' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine == 'x86_64' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-trtllm' and extra == 'extra-7-nemo-rl-vllm') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-trtllm') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-sglang') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-trtllm') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-trtllm') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-trtllm' and extra == 'extra-7-nemo-rl-vllm')" },
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