diff --git a/src/SparseSC/utils/match_space.py b/src/SparseSC/utils/match_space.py
index f1ab31d..eab6d43 100644
--- a/src/SparseSC/utils/match_space.py
+++ b/src/SparseSC/utils/match_space.py
@@ -204,7 +204,7 @@ def _MTLasso_MatchSpace(
     transformer = SelMatchSpace(m_sel)
     return transformer, V[m_sel], v_pen, (V, varselectorfit)
 
-def D_LassoCV_MatchSpace_factory(v_pens=None, n_v_cv=5, sample_frac=1, y_V_share=0.5):
+def D_LassoCV_MatchSpace_factory(v_pens=None, n_v_cv=5, sample_frac=1, y_V_share=0.5, fit_args={}):
     """
     Return a MatchSpace function that will fit a MultiTaskLassoCV for Y ~ X and Lasso of D_full ~ X_full
     and then combines the coefficients into weights using y_V_share
@@ -224,6 +224,7 @@ def _D_LassoCV_MatchSpace_wrapper(X, Y, **kwargs):
             n_v_cv=n_v_cv,
             sample_frac=sample_frac,
             y_V_share=y_V_share,
+            fit_args=fit_args,
             **kwargs
         )
 
@@ -231,7 +232,7 @@ def _D_LassoCV_MatchSpace_wrapper(X, Y, **kwargs):
 
 
 def _D_LassoCV_MatchSpace(
-    X, Y, X_full, D_full, v_pens=None, n_v_cv=5, sample_frac=1, y_V_share=0.5, **kwargs
+    X, Y, X_full, D_full, v_pens=None, n_v_cv=5, sample_frac=1, y_V_share=0.5, fit_args={}, **kwargs
 ):  # pylint: disable=missing-param-doc, unused-argument
     if sample_frac < 1:
         N_y = X.shape[0]
@@ -242,7 +243,7 @@ def _D_LassoCV_MatchSpace(
         sample_d = np.random.choice(N_d, int(sample_frac * N_d), replace=False)
         X_full = X_full[sample_d, :]
         D_full = D_full[sample_d]
-    y_varselectorfit = MultiTaskLassoCV(normalize=True, cv=n_v_cv, alphas=v_pens).fit(
+    y_varselectorfit = MultiTaskLassoCV(normalize=True, cv=n_v_cv, alphas=v_pens, **fit_args).fit(
         X, Y
     )
     y_V = np.sqrt(
@@ -465,7 +466,7 @@ def transform(self, X):
         return M
 
 
-def MTLassoMixed_MatchSpace_factory(v_pens=None, n_v_cv=5):
+def MTLassoMixed_MatchSpace_factory(v_pens=None, n_v_cv=5, fit_args={}):
     """
     Return a MatchSpace function that will fit a MultiTaskLassoCV for Y ~ X with the penalization optimized to reduce errors on goal units
 
@@ -476,17 +477,17 @@ def MTLassoMixed_MatchSpace_factory(v_pens=None, n_v_cv=5):
 
     def _MTLassoMixed_MatchSpace_wrapper(X, Y, fit_model_wrapper, **kwargs):
         return _MTLassoMixed_MatchSpace(
-            X, Y, fit_model_wrapper, v_pens=v_pens, n_v_cv=n_v_cv, **kwargs
+            X, Y, fit_model_wrapper, v_pens=v_pens, n_v_cv=n_v_cv, fit_args=fit_args, **kwargs
         )
 
     return _MTLassoMixed_MatchSpace_wrapper
 
 
 def _MTLassoMixed_MatchSpace(
-    X, Y, fit_model_wrapper, v_pens=None, n_v_cv=5, **kwargs
+    X, Y, fit_model_wrapper, v_pens=None, n_v_cv=5, fit_args={}, **kwargs
 ):  # pylint: disable=missing-param-doc, unused-argument
     # Note that MultiTaskLasso(CV).path with the same alpha doesn't produce same results as MultiTaskLasso(CV)
-    mtlasso_cv_fit = MultiTaskLassoCV(normalize=True, cv=n_v_cv, alphas=v_pens).fit(
+    mtlasso_cv_fit = MultiTaskLassoCV(normalize=True, cv=n_v_cv, alphas=v_pens, **fit_args).fit(
         X, Y
     )
     # V_cv = np.sqrt(np.sum(np.square(mtlasso_cv_fit.coef_), axis=0)) #n_tasks x n_features -> n_feature