diff --git a/examples/v1/config/sft_glm5p2.py b/examples/v1/config/sft_glm5p2.py
index a1a5d7064..4fff59ebf 100644
--- a/examples/v1/config/sft_glm5p2.py
+++ b/examples/v1/config/sft_glm5p2.py
@@ -8,6 +8,7 @@
 from xtuner.v1.loss import CELossConfig
 from xtuner.v1.model import get_model_config_from_hf
 from xtuner.v1.model.moe.glm52.indexer_chunk import resolve_indexer_topk_query_chunk_size
+from xtuner.v1.module.mtp import MTPConfig
 from xtuner.v1.train import TrainerConfig
 from xtuner.v1.train.trainer import LoadCheckpointConfig
 
@@ -49,6 +50,18 @@ def _get_float8_config() -> Float8Config | None:
 )
 
 model_cfg = get_model_config_from_hf(GLM5_2_MODEL_PATH)
+# The released checkpoint contains one *physical* MTP layer
+# (``num_nextn_predict_layers=1``).  GLM-5.2 training reuses that layer for
+# seven logical prediction depths; ``MTPConfig.num_layers`` controls the
+# recurrent depth while ``share_weights=True`` keeps construction/checkpoint
+# loading at one physical layer.
+model_cfg.mtp_config = MTPConfig(
+    num_layers=7,
+    share_weights=True,
+    detach_mtp_lm_head_weight=False,
+    detach_mtp_inputs=False,
+    loss_scaling_factor=0.1,
+)
 model_cfg.dispatcher = _get_dispatcher()
 model_cfg.ep_size = ep_size
 model_cfg.compile_cfg = _get_bool_env("MODEL_COMPILE", False)
diff --git a/tests/model/test_glm52_sft_mtp_config.py b/tests/model/test_glm52_sft_mtp_config.py
new file mode 100644
index 000000000..1b07acf52
--- /dev/null
+++ b/tests/model/test_glm52_sft_mtp_config.py
@@ -0,0 +1,108 @@
+"""Regression coverage for the canonical GLM-5.2 SFT recipe.
+
+The released checkpoint advertises one physical MTP layer through
+``num_nextn_predict_layers=1``.  The SFT recipe must keep that checkpoint
+layout while overriding XTuner's logical/recurrent depth to seven.
+"""
+
+import os
+import runpy
+import tempfile
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Any
+from unittest import mock
+
+
+RECIPE_PATH = Path(__file__).resolve().parents[2] / "examples/v1/config/sft_glm5p2.py"
+
+
+class _CaptureTrainerConfig:
+    """Small stand-in that records recipe arguments without initializing a trainer."""
+
+    def __init__(self, **kwargs: Any) -> None:
+        self.__dict__.update(kwargs)
+
+
+def _run_glm52_sft_recipe_regression() -> None:
+    """The SFT recipe should run one physical MTP block for seven depths."""
+    model_cfg = SimpleNamespace(
+        num_nextn_predict_layers=1,
+        mtp_config=SimpleNamespace(num_layers=1, share_weights=True),
+        attention=SimpleNamespace(),
+    )
+
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        tmp_path = Path(tmp_dir)
+        recipe_env = {
+            "GLM5_2_MODEL_PATH": str(tmp_path / "model"),
+            "ALPACA_PATH": str(tmp_path / "alpaca"),
+            "WORK_DIR": str(tmp_path / "work"),
+            "EP_SIZE": "1",
+            "INTRA_LAYER_MICRO_BATCH": "1",
+            "WORLD_SIZE": "1",
+            "GLOBAL_BATCH_SIZE": "1",
+            "SAMPLE_MAX_LENGTH": "4096",
+            "PACK_MAX_LENGTH": "16384",
+            "TOTAL_STEP": "10",
+            "LOSS_MODE": "chunk",
+            "LOSS_CHUNK_SIZE": "1024",
+            "FP8": "0",
+            "MODEL_COMPILE": "0",
+            "DATASET_TYPE": "alpaca",
+            "DATASET_SAMPLE_RATIO": "1.0",
+            "CACHE_TAG": "test",
+            "PACK_LEVEL": "soft",
+            "PACK_CHUNK_SIZE": "10000",
+            "DATALOADER_NUM_WORKERS": "0",
+            "PACK_WORKERS": "0",
+            "GLOBAL_PACK": "1",
+            "GROUP_BY_LENGTH": "1",
+            "LR": "1e-6",
+            "OPTIMIZER": "adamw",
+            "ADAMW_FOREACH": "0",
+            "SWAP_OPTIMIZER": "0",
+            "LR_TYPE": "cosine",
+            "WARMUP_RATIO": "0",
+            "CPU_OFFLOAD": "0",
+            "TORCH_COMPILE": "0",
+            "DISPATCHER": "none",
+            "SPARSE_MLA_BACKEND": "torch",
+            "SP_SIZE": "1",
+            "CHECKPOINT_INTERVAL": "200",
+            "CHECKPOINT_MAX_KEEP": "3",
+            "HF_INTERVAL": "200",
+            "HF_MAX_KEEP": "3",
+            "PROFILE_MEMORY": "0",
+            "PROFILE_TIME": "0",
+            "PROFILE_STEP": "2,3",
+            "DEBUG_SKIP_SAVE": "0",
+        }
+        # Deliberately omit these so they exercise the recipe's true defaults.
+        with mock.patch.dict(os.environ, recipe_env, clear=False):
+            os.environ.pop("STRICT_LOAD", None)
+            os.environ.pop("LOAD_CHECKPOINT_PATH", None)
+            with (
+                mock.patch("xtuner.v1.model.get_model_config_from_hf", return_value=model_cfg),
+                mock.patch("xtuner.v1.train.TrainerConfig", _CaptureTrainerConfig),
+            ):
+                namespace = runpy.run_path(str(RECIPE_PATH), run_name="__glm52_sft_recipe_test__")
+
+    trainer_cfg = namespace["trainer"]
+    recipe_model_cfg = namespace["model_cfg"]
+
+    assert recipe_model_cfg is model_cfg
+    assert trainer_cfg.model_cfg is model_cfg
+    assert model_cfg.num_nextn_predict_layers == 1
+    assert model_cfg.mtp_config.num_layers == 7
+    assert model_cfg.mtp_config.share_weights is True
+    assert model_cfg.mtp_config.detach_mtp_lm_head_weight is False
+    assert model_cfg.mtp_config.detach_mtp_inputs is False
+    assert model_cfg.mtp_config.loss_scaling_factor == 0.1
+    assert trainer_cfg.strict_load is True
+
+
+class TestGlm52SftMtpConfig:
+    def test_glm52_sft_recipe_uses_recurrent_mtp_depth_without_changing_checkpoint_depth(self) -> None:
+        """The SFT recipe should run one physical MTP block for seven depths."""
+        _run_glm52_sft_recipe_regression()