diff --git a/testdata/generate_training_data.py b/testdata/generate_training_data.py
index 8c0cbf318..6d64d0600 100755
--- a/testdata/generate_training_data.py
+++ b/testdata/generate_training_data.py
@@ -61,7 +61,6 @@
 def FindELFTrainingFiles():
   """ Returns the list of ELF files that should be used for training. These
   ELF files need to contain objdump-able debug information.
-
   """
   elf_files = [ filename for filename in glob.iglob(
     FLAGS.executable_directory + 'ELF/**/*', recursive=True)
@@ -350,7 +349,6 @@ def IndexToRowColumn(index, n):
   """
     Given an index into the non-zero elements of an upper triangular matrix,
     returns a tuple of integers indicating the row, column of that entry.
-
     n is the number of elements in the family we are dealing with.
   """
   if n & 1:
@@ -462,6 +460,8 @@ def GenerateRepulsionPairs( input_map, number_of_pairs ):
   while len(repulsion_set) != number_of_pairs and max_loop_iterations > 0:
     symbol_one, symbol_two = numpy.random.choice( symbols_as_list, 2,
       replace=False )
+    if (ExtractFunctionName(symbol_one) == ExtractFunctionName(symbol_two)):
+      continue
     element_one = random.choice( input_map[symbol_one] )
     element_two = random.choice( input_map[symbol_two] )
     ordered_pair = tuple(sorted([element_one, element_two]))
@@ -469,6 +469,13 @@ def GenerateRepulsionPairs( input_map, number_of_pairs ):
     max_loop_iterations = max_loop_iterations - 1
   return repulsion_set
 
+def ExtractFunctionName(symbol):
+  decoded_string = subprocess.run(["base64", "-d"], stdout=PIPE,
+    input=bytes(symbol, encoding="utf-8")).stdout.decode("utf-8")
+  decoded_string = decoded_string.split('(')[0]
+  return decoded_string
+
+
 def WritePairsFile( set_of_pairs, output_name ):
   """
   Take a set of pairs ((file_idA, addressA), (file_idB, addressB)) and write them
@@ -527,7 +534,6 @@ def WriteSeenTrainingAndValidationData(symbol_to_file_and_address, FLAGS):
      Remove random element R for the validation set
      Generate all pairs of attraction for the family without R (training)
      Generate all pairs of attraction between family members and R (validation)
-
      Now generate as many random repulsion pairs.
   """
   training_attraction_set = set()
@@ -611,7 +617,7 @@ def main(argv):
   # First, generate the training and validation data for performance on unseen
   # functions - to test how well we generalize beyond things we have already
   # seen variants of.
-  WriteUnseenTrainingAndValidationData(symbol_to_files_and_address, FLAGS)
+WriteUnseenTrainingAndValidationData(symbol_to_files_and_address, FLAGS)
 
   # Secondly, generate the training and validation data for performance on 'seen'
   # functions -- e.g. how well we perform if we need to spot a variant of a function
@@ -622,4 +628,3 @@ def main(argv):
 
 if __name__ == '__main__':
   app.run(main)
-