diff --git a/clrs/_src/baselines.py b/clrs/_src/baselines.py
index 97e30d7a..485b1443 100644
--- a/clrs/_src/baselines.py
+++ b/clrs/_src/baselines.py
@@ -144,6 +144,7 @@ def __init__(
       hidden_dim: int = 32,
       encode_hints: bool = False,
       decode_hints: bool = True,
+      compute_hint_loss: bool = True,
       encoder_init: str = 'default',
       use_lstm: bool = False,
       learning_rate: float = 0.005,
@@ -175,6 +176,7 @@ def __init__(
         message-passing vectors.
       encode_hints: Whether to provide hints as model inputs.
       decode_hints: Whether to provide hints as model outputs.
+      compute_hint_loss: Whether to add hint loss to the output loss.
       encoder_init: The initialiser type to use for the encoders.
       use_lstm: Whether to insert an LSTM after message passing.
       learning_rate: Learning rate for training.
@@ -202,15 +204,19 @@ def __init__(
 
     Raises:
       ValueError: if `encode_hints=True` and `decode_hints=False`.
+                  if `compute_hint_loss=True` and `decode_hints=False`.
     """
     super(BaselineModel, self).__init__(spec=spec)
 
     if encode_hints and not decode_hints:
       raise ValueError('`encode_hints=True`, `decode_hints=False` is invalid.')
+    if compute_hint_loss and not decode_hints:
+      raise ValueError('`compute_hint_loss=True`, `decode_hints=False` is invalid.')
 
     assert hint_repred_mode in ['soft', 'hard', 'hard_on_eval']
 
     self.decode_hints = decode_hints
+    self.compute_hint_loss = compute_hint_loss
     self.checkpoint_path = checkpoint_path
     self.name = name
     self._freeze_processor = freeze_processor
@@ -414,7 +420,7 @@ def _loss(self, params, rng_key, feedback, algorithm_index):
       )
 
     # Optionally accumulate hint losses.
-    if self.decode_hints:
+    if self.compute_hint_loss:
       for truth in feedback.features.hints:
         total_loss += losses.hint_loss(
             truth=truth,
@@ -455,7 +461,7 @@ def verbose_loss(self, feedback: _Feedback, extra_info) -> Dict[str, _Array]:
     losses_ = {}
 
     # Optionally accumulate hint losses.
-    if self.decode_hints:
+    if self.compute_hint_loss:
       for truth in feedback.features.hints:
         losses_.update(
             losses.hint_loss(
@@ -591,7 +597,7 @@ def _loss(self, params, rng_key, feedback, mp_state, algorithm_index):
       )
 
     # Optionally accumulate hint losses.
-    if self.decode_hints:
+    if self.compute_hint_loss:
       for truth in feedback.features.hints:
         loss = losses.hint_loss_chunked(
             truth=truth,
diff --git a/clrs/examples/run.py b/clrs/examples/run.py
index 2173e8d3..59c100bd 100644
--- a/clrs/examples/run.py
+++ b/clrs/examples/run.py
@@ -73,7 +73,7 @@
                    'during training instead of predicted hints. Only '
                    'pertinent in encoded_decoded modes.')
 flags.DEFINE_enum('hint_mode', 'encoded_decoded',
-                  ['encoded_decoded', 'decoded_only', 'none'],
+                  ['encoded_decoded', 'decoded_only', 'none_encoded_decoded', 'none'],
                   'How should hints be used? Note, each mode defines a '
                   'separate task, with various difficulties. `encoded_decoded` '
                   'requires the model to explicitly materialise hint sequences '
@@ -85,7 +85,9 @@
                   'note that we currently do not make any efforts to '
                   'counterbalance the various hint losses. Hence, for certain '
                   'tasks, the best performance will now be achievable with no '
-                  'hint usage at all (`none`).')
+                  'hint usage at all (`none`). `none_encoded_decoded` produces'
+                  'similar computation graphs to `encoded_decoded` but without'
+                  'hint usage.')
 flags.DEFINE_enum('hint_repred_mode', 'soft', ['soft', 'hard', 'hard_on_eval'],
                   'How to process predicted hints when fed back as inputs.'
                   'In soft mode, we use softmaxes for categoricals, pointers '
@@ -365,14 +367,21 @@ def main(unused_argv):
   if FLAGS.hint_mode == 'encoded_decoded':
     encode_hints = True
     decode_hints = True
+    compute_hint_loss = True
   elif FLAGS.hint_mode == 'decoded_only':
     encode_hints = False
     decode_hints = True
+    compute_hint_loss = True
+  elif FLAGS.hint_mode == 'none_encoded_decoded':
+    encode_hints = True
+    decode_hints = True
+    compute_hint_loss = False
   elif FLAGS.hint_mode == 'none':
     encode_hints = False
     decode_hints = False
+    compute_hint_loss = False
   else:
-    raise ValueError('Hint mode not in {encoded_decoded, decoded_only, none}.')
+    raise ValueError('Hint mode not in {encoded_decoded, decoded_only, none_encoded_decoded, none}.')
 
   train_lengths = [int(x) for x in FLAGS.train_lengths]
 
@@ -396,6 +405,7 @@ def main(unused_argv):
       hidden_dim=FLAGS.hidden_size,
       encode_hints=encode_hints,
       decode_hints=decode_hints,
+      compute_hint_loss=compute_hint_loss,
       encoder_init=FLAGS.encoder_init,
       use_lstm=FLAGS.use_lstm,
       learning_rate=FLAGS.learning_rate,