diff --git a/vall_e/config.py b/vall_e/config.py
index 7816bd4..180260f 100755
--- a/vall_e/config.py
+++ b/vall_e/config.py
@@ -274,6 +274,9 @@ class ModelExperimentalSettings:
 	# this should allow for "faster" training as each sample is trained entirely, but slower backwards (and possibly less stable training, maybe)
 	monolithic_audio_encoder: bool = False # combines the prom/resp embeddings into one unit
 	# this usually sounds bad, as the model can "extract" features from the prom separate from the ones in the resp
+	predict_causally: bool = False # predicts the next token even for the non-causal/NAR tasks, in theory this should also bolster the model, as
+	# * NAR-demask would semi-doubly train for AR
+	# * the model wouldn't also need to learn when to predict the token in place
 
 	# these technically should be as hyperparameters
 	# performs token dropout to compensate for errors
diff --git a/vall_e/models/ar_nar.py b/vall_e/models/ar_nar.py
index dead515..eb36b7a 100644
--- a/vall_e/models/ar_nar.py
+++ b/vall_e/models/ar_nar.py
@@ -171,7 +171,7 @@ class AR_NAR(Base):
 							resps_list[i][t, l] = clamp(token + offset, 1, 1022) # +- 1
 
 			# only apply stop token for RVQ level 0
-			if (self.version < 7 and quant_level <= 0 and timesteps[i] is None) or (self.version >= 7):
+			if (self.version < 7 and quant_level <= 0 and timesteps[i] is None) or (self.version >= 7 and timesteps[i] is None) or (self.predict_causally):
 				# append stop tokens for AR
 				if task not in text_task:
 					resps_list[i] = torch.cat([ resps, audio_stop_sequence ])
diff --git a/vall_e/models/base.py b/vall_e/models/base.py
index c680a3d..5088e61 100755
--- a/vall_e/models/base.py
+++ b/vall_e/models/base.py
@@ -581,9 +581,11 @@ class Base(nn.Module):
 		ignore_inputs_for_loss = self.config.experimental.ignore_inputs_for_loss if self.config is not None else False
 		
 		resp_parallel_training = self.config.experimental.resp_parallel_training if self.config is not None else True
+		predict_causally = self.config.experimental.predict_causally if self.config is not None else False
 		monolithic_audio_encoder = self.config.experimental.monolithic_audio_encoder if self.config is not None else False
 
 		self.resp_parallel_training = resp_parallel_training
+		self.predict_causally = self.predict_causally
 
 		n_tasks = self.config.tasks if self.config is not None else 8
 		n_langs = self.config.langs if self.config is not None else 2
@@ -1503,7 +1505,7 @@ class Base(nn.Module):
 				return None, None
 
 			# shift if causal
-			if causal or self.version >= 7:
+			if causal or self.predict_causally:
 				l = self.causal_size
 				logit = logit[..., :-l, :] # shift the target so that token n...
 				sequence = sequence[..., l:] # ...predicts token n + 1