diff --git a/lpm_kernel/L2/dpo/dpo_train.py b/lpm_kernel/L2/dpo/dpo_train.py
index fb5d0bc1..86826989 100644
--- a/lpm_kernel/L2/dpo/dpo_train.py
+++ b/lpm_kernel/L2/dpo/dpo_train.py
@@ -42,13 +42,18 @@ def training_data_processor(args, SYS = "You are a helpful assistant.\n\n"):
     return training_data
 
 def train(args):
+    # Read CUDA toggle from training params
+    training_params = TrainingParamsManager.get_latest_training_params()
+    use_cuda = training_params.get('use_cuda', False)
+    device = torch.device('cuda' if use_cuda and torch.cuda.is_available() else 'cpu')
+
     tokenizer = AutoTokenizer.from_pretrained(args.base_model_path, padding_side="left")
     model = AutoModelForCausalLM.from_pretrained(
-    args.base_model_path, 
-    trust_remote_code=True,
-    ignore_mismatched_sizes=True, 
-    torch_dtype=torch.float32,  # CPU doesn't support bfloat16
-)
+        args.base_model_path, 
+        trust_remote_code=True,
+        ignore_mismatched_sizes=True, 
+        torch_dtype="auto",
+    ).to(device)
     time_str = get_east_eight_time_formatted()
 
     # merged_model = model.merge_and_unload()
diff --git a/lpm_kernel/api/domains/trainprocess/routes.py b/lpm_kernel/api/domains/trainprocess/routes.py
index af6425e4..c8a12f2e 100644
--- a/lpm_kernel/api/domains/trainprocess/routes.py
+++ b/lpm_kernel/api/domains/trainprocess/routes.py
@@ -288,7 +288,6 @@ def retrain():
         data_synthesis_mode: Mode for data synthesis (optional)
         use_cuda: Whether to use CUDA for training (optional)
         is_cot: Whether to use Chain of Thought (optional)
-        use_previous_params: Whether to use previous training parameters (optional, default True)
     
     Returns:
         Response: JSON response
@@ -318,7 +317,7 @@ def retrain():
         is_cot = data.get("is_cot", None)
         
         # Log the received parameters
-        logger.info(f"Retrain parameters: model_name={model_name}, learning_rate={learning_rate}, number_of_epochs={number_of_epochs}, concurrency_threads={concurrency_threads}, data_synthesis_mode={data_synthesis_mode}, use_cuda={use_cuda}, is_cot={is_cot}, use_previous_params={use_previous_params}")
+        logger.info(f"Retrain parameters: model_name={model_name}, learning_rate={learning_rate}, number_of_epochs={number_of_epochs}, concurrency_threads={concurrency_threads}, data_synthesis_mode={data_synthesis_mode}, use_cuda={use_cuda}, is_cot={is_cot}")
         
         # Create training service instance
         train_service = TrainProcessService(current_model_name=model_name)