diff --git a/src/llamafactory/extras/constants.py b/src/llamafactory/extras/constants.py
index e78c8b908..00193c276 100644
--- a/src/llamafactory/extras/constants.py
+++ b/src/llamafactory/extras/constants.py
@@ -63,6 +63,7 @@ MCA_SUPPORTED_MODELS = {
     "qwen2",
     "qwen2_vl",
     "qwen2_5_vl",
+    "qwen3_vl",
     "qwen3",
     "qwen3_moe",
     "qwen3_next",
diff --git a/src/llamafactory/train/mca/workflow.py b/src/llamafactory/train/mca/workflow.py
index 4684e827d..affa2efc8 100644
--- a/src/llamafactory/train/mca/workflow.py
+++ b/src/llamafactory/train/mca/workflow.py
@@ -11,14 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""MCA (mcore_adapter) workflows for PT/SFT/DPO stages, aligned with LLaMA-Factory's workflow style."""
-
-from __future__ import annotations
 
 import functools
 from collections.abc import Sequence
 from copy import deepcopy
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Optional
+
+from transformers import DataCollatorForSeq2Seq
 
 from ...data import (
     SFTDataCollatorWith4DAttentionMask,
@@ -44,11 +43,11 @@ from mcore_adapter.models import AutoConfig, AutoModel
 from mcore_adapter.trainer import DPOTrainer as McaDPOTrainer
 from mcore_adapter.trainer import McaTrainer
 from mcore_adapter.trainer.dpo_config import DPOConfig
-from mcore_adapter.training_args import Seq2SeqTrainingArguments as McaSeq2SeqTrainingArguments
 
 
 if TYPE_CHECKING:
-    from transformers import DataCollatorForSeq2Seq, TrainerCallback
+    from mcore_adapter.training_args import Seq2SeqTrainingArguments as McaSeq2SeqTrainingArguments
+    from transformers import TrainerCallback
 
     from ...hparams import DataArguments, FinetuningArguments, ModelArguments
 
@@ -76,7 +75,7 @@ def _data_collator_wrapper(data_collator: Any):
     return wrapper
 
 
-def _check_model_support(model_args: ModelArguments):
+def _check_model_support(model_args: "ModelArguments"):
     from transformers import AutoConfig as HfAutoConfig
 
     config = HfAutoConfig.from_pretrained(
@@ -87,11 +86,11 @@ def _check_model_support(model_args: ModelArguments):
 
 
 def run_pt(
-    model_args: ModelArguments,
-    data_args: DataArguments,
-    training_args: McaSeq2SeqTrainingArguments,
-    finetuning_args: FinetuningArguments,
-    callbacks: list[TrainerCallback] | None = None,
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "McaSeq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    callbacks: Optional[list["TrainerCallback"]] = None,
 ):
     tokenizer_module = load_tokenizer(model_args)
     tokenizer = tokenizer_module["tokenizer"]
@@ -104,10 +103,7 @@ def run_pt(
 
     _check_model_support(model_args)
     model = AutoModel.from_pretrained(model_args.model_name_or_path, training_args)
-
-    from transformers import DataCollatorForSeq2Seq
-
-    data_collator: DataCollatorForSeq2Seq = DataCollatorForSeq2Seq(
+    data_collator = DataCollatorForSeq2Seq(
         tokenizer=tokenizer,
         pad_to_multiple_of=8,
         label_pad_token_id=IGNORE_INDEX,
@@ -142,11 +138,11 @@ def run_pt(
 
 
 def run_sft(
-    model_args: ModelArguments,
-    data_args: DataArguments,
-    training_args: McaSeq2SeqTrainingArguments,
-    finetuning_args: FinetuningArguments,
-    callbacks: list[TrainerCallback] | None = None,
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "McaSeq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    callbacks: Optional[list["TrainerCallback"]] = None,
 ):
     # align packing flags
     # TODO: FIX SequencePacking
@@ -166,7 +162,7 @@ def run_sft(
     model = AutoModel.from_pretrained(model_args.model_name_or_path, training_args)
 
     # optional freezing for qwen2_vl, qwen2_5_vl
-    if getattr(model.config, "hf_model_type", None) in ["qwen2_vl", "qwen2_5_vl"]:
+    if getattr(model.config, "hf_model_type", None) in ["qwen2_vl", "qwen2_5_vl", "qwen3_vl"]:
         params_to_freeze = []
         if finetuning_args.freeze_vision_tower:
             params_to_freeze.extend(["vision_model.blocks", "vision_model.patch_embed"])
@@ -220,11 +216,11 @@ def run_sft(
 
 
 def run_dpo(
-    model_args: ModelArguments,
-    data_args: DataArguments,
-    training_args: McaSeq2SeqTrainingArguments,
-    finetuning_args: FinetuningArguments,
-    callbacks: list[TrainerCallback] | None = None,
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "McaSeq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    callbacks: Optional[list["TrainerCallback"]] = None,
 ):
     tokenizer_module = load_tokenizer(model_args)
     tokenizer = tokenizer_module["tokenizer"]