diff --git a/scripts/train_model.py b/scripts/train_model.py
index 89d80b8..f0081e8 100644
--- a/scripts/train_model.py
+++ b/scripts/train_model.py
@@ -22,6 +22,7 @@ from sklearn.metrics import roc_auc_score, classification_report
 from src.indicators import Indicators
 from src.ml_features import build_features, FEATURE_COLS
 from src.label_builder import build_labels
+from src.dataset_builder import generate_dataset_vectorized
 
 def _cgroup_cpu_count() -> int:
     """cgroup v1/v2 쿼터를 읽어 실제 할당된 CPU 수를 반환한다.
@@ -107,7 +108,8 @@ def generate_dataset(df: pd.DataFrame, n_jobs: int | None = None) -> pd.DataFram
     total = len(df)
     indices = range(60, total - LOOKAHEAD)
 
-    workers = n_jobs or max(1, _cgroup_cpu_count() - 1)
+    # M4 mini: 10코어(P4+E6). 너무 많은 worker는 IPC 오버헤드를 늘리므로 8로 제한
+    workers = n_jobs or min(max(1, _cgroup_cpu_count() - 1), 8)
     print(f"  병렬 처리: {workers}코어 사용 (총 {len(indices):,}개 인덱스)")
 
     # DataFrame을 numpy로 변환해서 worker 간 전달 비용 최소화
@@ -117,7 +119,8 @@ def generate_dataset(df: pd.DataFrame, n_jobs: int | None = None) -> pd.DataFram
 
     rows = []
     errors = []
-    chunk = max(1, len(task_args) // (workers * 10))
+    # chunksize를 크게 잡아 IPC 직렬화 횟수를 줄임
+    chunk = max(100, len(task_args) // workers)
     with Pool(processes=workers) as pool:
         for idx, result in enumerate(pool.imap(_process_index, task_args, chunksize=chunk)):
             if isinstance(result, dict):
@@ -143,13 +146,13 @@ def generate_dataset(df: pd.DataFrame, n_jobs: int | None = None) -> pd.DataFram
     return pd.DataFrame(rows)
 
 
-def train(data_path: str, n_jobs: int | None = None):
+def train(data_path: str):
     print(f"데이터 로드: {data_path}")
     df = pd.read_parquet(data_path)
     print(f"캔들 수: {len(df)}")
 
     print("데이터셋 생성 중...")
-    dataset = generate_dataset(df, n_jobs=n_jobs)
+    dataset = generate_dataset_vectorized(df)
 
     if dataset.empty or "label" not in dataset.columns:
         raise ValueError(f"데이터셋 생성 실패: 샘플 0개. 위 오류 메시지를 확인하세요.")
@@ -216,10 +219,8 @@ def train(data_path: str, n_jobs: int | None = None):
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("--data", default="data/xrpusdt_1m.parquet")
-    parser.add_argument("--jobs", type=int, default=None,
-                        help="병렬 worker 수 (기본: CPU 수 - 1)")
     args = parser.parse_args()
-    train(args.data, n_jobs=args.jobs)
+    train(args.data)
 
 
 if __name__ == "__main__":