diff --git a/n3fit/src/n3fit/scripts/n3fit_exec.py b/n3fit/src/n3fit/scripts/n3fit_exec.py
index c57445e627..264acb4eee 100755
--- a/n3fit/src/n3fit/scripts/n3fit_exec.py
+++ b/n3fit/src/n3fit/scripts/n3fit_exec.py
@@ -147,10 +147,13 @@ def from_yaml(cls, o, *args, **kwargs):
             N3FIT_FIXED_CONFIG['actions_'].extend((training_action, validation_action))
         #Theorycovmat flags and defaults
         N3FIT_FIXED_CONFIG['theory_covmat_flag'] = False
+        N3FIT_FIXED_CONFIG['use_thcovmat_in_fitting'] = False
+        N3FIT_FIXED_CONFIG['use_thcovmat_in_sampling'] = False
         if (thconfig:=file_content.get('theorycovmatconfig')) is not None:
-            N3FIT_FIXED_CONFIG['theory_covmat_flag'] = True
             N3FIT_FIXED_CONFIG['use_thcovmat_in_fitting'] = thconfig.get('use_thcovmat_in_fitting', True) 
-            N3FIT_FIXED_CONFIG['use_thcovmat_in_sampling'] = thconfig.get('use_thcovmat_in_sampling', True)  
+            N3FIT_FIXED_CONFIG['use_thcovmat_in_sampling'] = thconfig.get('use_thcovmat_in_sampling', True) 
+            if N3FIT_FIXED_CONFIG['use_thcovmat_in_sampling'] or N3FIT_FIXED_CONFIG['use_thcovmat_in_fitting']:
+                N3FIT_FIXED_CONFIG['theory_covmat_flag'] = True
             N3FIT_FIXED_CONFIG['use_user_uncertainties'] = thconfig.get('use_user_uncertainties', False) 
             N3FIT_FIXED_CONFIG['use_scalevar_uncertainties'] = thconfig.get('use_scalevar_uncertainties', True) 
         #Sampling flags
diff --git a/validphys2/src/validphys/config.py b/validphys2/src/validphys/config.py
index 556db6a068..9bdc6532e2 100644
--- a/validphys2/src/validphys/config.py
+++ b/validphys2/src/validphys/config.py
@@ -1584,24 +1584,29 @@ def load_default_data_grouping(self, spec):
         # somebody will want to add to this at some point e.g for th. uncertainties
         allowed = {
             "standard_report": "experiment",
+            "thcovmat_fit": "ALL"
         }
         return allowed[spec]
 
     def produce_processed_data_grouping(
-        self, data_grouping=None, data_grouping_recorded_spec_=None
+        self, use_thcovmat_in_fitting=False, use_thcovmat_in_sampling=False, data_grouping=None, data_grouping_recorded_spec_=None
     ):
         """Process the data_grouping key from the runcard, or lockfile. If
         `data_grouping_recorded_spec_` is present then its value is taken, and
         the runcard is assumed to be a lockfile.
 
-        If data_grouping is None, then fall back to old behaviour of grouping
-        by experiment.
+        If data_grouping is None, then, if either use_thcovmat_in_fitting or use_thcovmat_in_sampling
+        (or both) are true (which means that the fit is a thcovmat fit), group all the datasets 
+        together, otherwise fall back to the default behaviour of grouping by 
+        experiment (called standard_report).
 
         Else, the user can specfiy their own grouping, for example metadata_process.
         """
         if data_grouping is None:
             # fallback to old default behaviour, but still record to lockfile
             data_grouping = self.parse_data_grouping("standard_report")
+            if use_thcovmat_in_fitting or use_thcovmat_in_sampling:
+                data_grouping = self.parse_data_grouping("thcovmat_fit")
         if data_grouping_recorded_spec_ is not None:
             return data_grouping_recorded_spec_[data_grouping]
         return self.load_default_data_grouping(data_grouping)
@@ -1653,17 +1658,6 @@ def produce_group_dataset_inputs_by_metadata(
             for name, group in res.items()
         ]
 
-    def produce_group_dataset_inputs_by_fitting_group(
-        self, data_input, theory_covmat_flag
-    ):
-        """
-        Groups datasets all together in a group called ALL if the theory covariance matrix
-        is used in the fit, otherwise it groups them by experiment.
-        """
-        if theory_covmat_flag:
-            return self.produce_group_dataset_inputs_by_metadata(data_input, "ALL")
-        return self.produce_group_dataset_inputs_by_metadata(data_input, "experiment")
-
     def produce_fivetheories(self, point_prescription):
         if point_prescription == "5bar point":
             return "bar"
diff --git a/validphys2/src/validphys/n3fit_data.py b/validphys2/src/validphys/n3fit_data.py
index ef65723418..9a8633e7d9 100644
--- a/validphys2/src/validphys/n3fit_data.py
+++ b/validphys2/src/validphys/n3fit_data.py
@@ -311,7 +311,7 @@ def fitting_data_dict(
     return dict_out
 
 
-exps_fitting_data_dict = collect("fitting_data_dict", ("group_dataset_inputs_by_fitting_group",))
+exps_fitting_data_dict = collect("fitting_data_dict", ("group_dataset_inputs_by_metadata",))
 
 
 def replica_nnseed_fitting_data_dict(replica, exps_fitting_data_dict, replica_nnseed):
diff --git a/validphys2/src/validphys/tests/conftest.py b/validphys2/src/validphys/tests/conftest.py
index c96d8b70bc..080048efd3 100644
--- a/validphys2/src/validphys/tests/conftest.py
+++ b/validphys2/src/validphys/tests/conftest.py
@@ -58,7 +58,6 @@ def tmp(tmpdir):
         use_cuts='nocuts',
         dataset_inputs=DATA,
         theoryid=THEORYID,
-        use_fitthcovmat=False,
         Q=10,
     )