diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 79d4e78..f9499b5 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -1831,10 +1831,10 @@
             and "data_gen" in testGen.TOSA_OP_LIST[opName]
             and gtu.dtypeIsSupportedByCompliance(dtype)
         ):
-            if gtu.dtypeIsFloat(dtype):
-                dataGenTypesList = testGen.TOSA_OP_LIST[opName]["data_gen"]["fp"]
-            else:
-                dataGenTypesList = testGen.TOSA_OP_LIST[opName]["data_gen"]["int"]
+            dataGenTypesList = testGen.TOSA_OP_LIST[opName]["data_gen"].get(
+                dtype, (gtu.DataGenType.PSEUDO_RANDOM,)
+            )
+
         else:
             # Error test or No data generator types listed - assume random
             dataGenTypesList = (gtu.DataGenType.PSEUDO_RANDOM,)
@@ -1843,16 +1843,7 @@
         new_arg_list = []
         for dg_type in dataGenTypesList:
             for arg_str, args_dict in arg_list:
-
-                if dg_type == gtu.DataGenType.FULL_RANGE:
-                    tensor_size = gtu.product(shapeList[0])
-                    if tensor_size >= gtu.DTYPE_ATTRIBUTES[dtype]["fullset"]:
-                        # Large enough tensor data size for full range, add a single test
-                        num_test_sets = 0
-                    else:
-                        # Not enough data size for full range of values, revert to random numbers
-                        dg_type = gtu.DataGenType.PSEUDO_RANDOM
-
+                gen_args_dict = args_dict.copy()
                 if dg_type == gtu.DataGenType.PSEUDO_RANDOM:
                     if error_name is None:
                         num_test_sets = (
@@ -1883,18 +1874,31 @@
 
                     num_test_sets = testGen.TOSA_MI_DOT_PRODUCT_TEST_SETS
 
+                elif dg_type == gtu.DataGenType.FULL_RANGE:
+                    tensor_size = gtu.product(shapeList[0])
+                    if tensor_size < gtu.DTYPE_ATTRIBUTES[dtype]["fullset"]:
+                        shape_info = " ({})".format(shapeList[0])
+                        logger.info(
+                            f"Skipping {opName}{shape_info} as tensor data size too small for full range of values {tensor_size} < {gtu.DTYPE_ATTRIBUTES[dtype]['fullset']}"
+                        )
+                        continue
+                    # Large enough tensor data size for full range, add a single test
+                    num_test_sets = 0
+                    arg_str = f"{arg_str}_full" if arg_str else "full"
+                    gen_args_dict["tags"] = args_dict.get("tags", []) + [
+                        "non_finite_fp_data"
+                    ]
+
+                gen_args_dict["dg_type"] = dg_type
                 if num_test_sets > 0:
                     for s in range(0, num_test_sets):
                         set_arg_str = f"{arg_str}_s{s}" if arg_str else f"s{s}"
-                        set_args_dict = args_dict.copy()
+                        set_args_dict = gen_args_dict.copy()
                         set_args_dict["s"] = s
-                        set_args_dict["dg_type"] = dg_type
                         new_arg_list.append((set_arg_str, set_args_dict))
                 else:
                     # Default is a single test
-                    new_args_dict = args_dict.copy()
-                    new_args_dict["dg_type"] = dg_type
-                    new_arg_list.append((arg_str, new_args_dict))
+                    new_arg_list.append((arg_str, gen_args_dict))
 
         return new_arg_list