diff --git a/.github/workflows/tutorial-check.yml b/.github/workflows/tutorial-check.yml
index c24cab8..67bbcf8 100644
--- a/.github/workflows/tutorial-check.yml
+++ b/.github/workflows/tutorial-check.yml
@@ -33,8 +33,14 @@ jobs:
         python -m pip install --upgrade pip
         pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
         pip install -e ".[yuanrong]"
-    - name: Run tutorials
+        pip install nbconvert ipykernel
+    - name: Run Python tutorials
       run: |
         export TQ_NUM_THREADS=2
         export RAY_DEDUP_LOGS=0
-        for file in tutorial/*.py; do python3 "$file"; done
\ No newline at end of file
+        for file in tutorial/*.py; do python3 "$file"; done
+    - name: Run notebook tutorials
+      run: |
+        export TQ_NUM_THREADS=2
+        export RAY_DEDUP_LOGS=0
+        jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=120 tutorial/basic.ipynb
\ No newline at end of file
diff --git a/tests/e2e/test_kv_interface_e2e.py b/tests/e2e/test_kv_interface_e2e.py
index 4fac9d2..6ace099 100644
--- a/tests/e2e/test_kv_interface_e2e.py
+++ b/tests/e2e/test_kv_interface_e2e.py
@@ -178,6 +178,15 @@ def assert_tensor_close(tensor_a, tensor_b, rtol=1e-5, atol=1e-8, msg=""):
     assert torch.allclose(tensor_a, tensor_b, rtol=rtol, atol=atol), f"{msg} Tensors are not close"
 
 
+def assert_nested_tensor_equal(nested_a, nested_b, msg=""):
+    """Assert two nested (jagged) tensors are equal component-wise."""
+    components_a = list(nested_a)
+    components_b = list(nested_b)
+    assert len(components_a) == len(components_b), f"{msg} Length mismatch: {len(components_a)} vs {len(components_b)}"
+    for i, (a, b) in enumerate(zip(components_a, components_b, strict=True)):
+        assert torch.equal(a, b), f"{msg} Component {i} not equal: {a} vs {b}"
+
+
 class TestKVPutE2E:
     """End-to-end tests for kv_put functionality."""
 
@@ -518,6 +527,25 @@ def test_kv_batch_put_returns_cumulative_fields(self, controller, tq_api):
 class TestKVGetE2E:
     """End-to-end tests for kv_batch_get functionality."""
 
+    def test_kv_batch_get_nested_tensor(self, controller, tq_api):
+        # test put a regular tensor with batch size 1 and get it back as a nested tensor
+        partition_id = "test_partition"
+        keys = []
+        data_list = []
+
+        for i in range(1, 4):
+            key = f"nested_tensor_{i}"
+            keys.append(key)
+            data = torch.randn(size=(i,))
+            data_list.append(data)
+            fields = TensorDict({"data": data.unsqueeze(0)}, batch_size=1)
+            tq_api.kv_put(key=key, partition_id=partition_id, fields=fields, tag=None)
+
+        retrieved = tq_api.kv_batch_get(keys=keys, partition_id=partition_id)
+
+        assert_nested_tensor_equal(retrieved["data"], torch.nested.as_nested_tensor(data_list, layout=torch.jagged))
+        tq_api.kv_clear(keys=keys, partition_id=partition_id)
+
     def test_kv_batch_get_single_key(self, controller, tq_api):
         """Test getting data for a single key."""
         partition_id = "test_partition"
@@ -569,11 +597,8 @@ def test_kv_batch_get_partial_keys(self, controller, tq_api):
         retrieved = tq_api.kv_batch_get(keys=partial_keys, partition_id=partition_id)
         assert_tensor_equal(retrieved["data"], expected_data)
 
-        for actual, expected in zip(retrieved["nested_data"], expected_nested_data, strict=True):
-            assert_tensor_equal(actual, expected)
-
-        for actual, expected in zip(retrieved["three_d_nested_data"], expected_three_d_nested_data, strict=True):
-            assert_tensor_equal(actual, expected)
+        assert_nested_tensor_equal(retrieved["nested_data"], expected_nested_data)
+        assert_nested_tensor_equal(retrieved["three_d_nested_data"], expected_three_d_nested_data)
 
         tq_api.kv_clear(keys=keys, partition_id=partition_id)
 
diff --git a/tutorial/basic.ipynb b/tutorial/basic.ipynb
index aaa5d44..5e5944d 100644
--- a/tutorial/basic.ipynb
+++ b/tutorial/basic.ipynb
@@ -49,7 +49,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2026-03-25 15:57:42,882\tINFO worker.py:2014 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n",
+      "2026-03-27 23:06:04,557\tINFO worker.py:2014 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n",
       "/opt/miniconda3/envs/verl/lib/python3.11/site-packages/ray/_private/worker.py:2062: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0\n",
       "  warnings.warn(\n"
      ]
@@ -58,7 +58,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33m(raylet)\u001b[0m It looks like you're creating a detached actor in an anonymous namespace. In order to access this actor in the future, you will need to explicitly connect to this namespace with ray.init(namespace=\"d7384875-7cae-4308-b16c-c92541dc9f07\", ...)\n",
+      "\u001b[33m(raylet)\u001b[0m It looks like you're creating a detached actor in an anonymous namespace. In order to access this actor in the future, you will need to explicitly connect to this namespace with ray.init(namespace=\"798d49c0-a4e8-4877-813b-bce2d966c4eb\", ...)\n",
       "TransferQueue is ready!\n"
      ]
     }
@@ -386,7 +386,7 @@
    ],
    "source": [
     "# Retrieve only input_ids (single field)\n",
-    "result = tq.kv_batch_get(keys=\"sample_1\", partition_id=\"train\", fields=\"input_ids\")\n",
+    "result = tq.kv_batch_get(keys=\"sample_1\", partition_id=\"train\", select_fields=\"input_ids\")\n",
     "print(\"Fields returned:\", list(result.keys()))\n",
     "assert \"input_ids\" in result.keys()\n",
     "assert \"attention_mask\" not in result.keys()\n",
@@ -395,7 +395,7 @@
     "result = tq.kv_batch_get(\n",
     "    keys=\"sample_1\",\n",
     "    partition_id=\"train\",\n",
-    "    fields=[\"input_ids\", \"attention_mask\"],\n",
+    "    select_fields=[\"input_ids\", \"attention_mask\"],\n",
     ")\n",
     "print(\"Fields returned:\", list(result.keys()))"
    ]
@@ -969,7 +969,7 @@
     "| Init | `tq.init(config)` | Call once; subsequent processes auto-connect |\n",
     "| Put single | `tq.kv_put(key, partition_id, fields, tag)` | `fields` can be a plain dict |\n",
     "| Put batch | `tq.kv_batch_put(keys, partition_id, fields, tags)` | `fields` must be a `TensorDict` |\n",
-    "| Get | `tq.kv_batch_get(keys, partition_id, fields=None)` | Returns a `TensorDict` |\n",
+    "| Get | `tq.kv_batch_get(keys, partition_id, select_fields=None)` | Returns a `TensorDict` |\n",
     "| List | `tq.kv_list(partition_id=None)` | Returns `{partition: {key: tag}}` |\n",
     "| Clear | `tq.kv_clear(keys, partition_id)` | Removes keys + data |\n",
     "| Close | `tq.close()` | Tears down controller & storage |\n",
@@ -980,6 +980,13 @@
     "For low-level, metadata-based access, see `tq.get_client()` and the\n",
     "[official tutorials](https://github.com/Ascend/TransferQueue/tree/main/tutorial)."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {