diff --git a/vall_e/models/arch/llama.py b/vall_e/models/arch/llama.py
index 81fb5bd..3da812d 100644
--- a/vall_e/models/arch/llama.py
+++ b/vall_e/models/arch/llama.py
@@ -322,15 +322,23 @@ class LlamaModel_Adapted(LlamaModel):
 		return random.random() < P
 
 	# to-do: properly implement this per the paper
-	# this probably is a function of layer number and training step to decide what layer to apply layerskip to for training
-	def cirriculum( self, l, t=0 ):
-		return 1 # self.layers_n - 1
+	# there doesn't seem /too/ bad of a performance hit, but the paper mentions it affecting accuracy of the last layer if all layers had early exit
+	def cirriculum( self, l, t=None, R=2 ):
+		if t is None:
+			return 1
+		
+		# YUCK
+		# this guarantees at least R layers are active at all intervals, which is important because this gives a division by zero otherwise
+		for i in range(R):
+			if l == ((t % self.layers_n) + i * (self.layers_n // R)) % self.layers_n:
+				return 1
+		return 0
 
-	def early_exit_loss( self, losses, t=0 ):
+	def early_exit_loss( self, losses, t=None ):
 		return sum([ self.normalized_per_layer_loss_scale( l, t ) * losses[l] for l in range(0, self.layers_n) ])
 
-	def normalized_per_layer_loss_scale( self, l, t=0 ):
-		return (self.cirriculum(l, t) * self.early_exit_factor( l )) / (sum([ self.cirriculum(l, t) * self.early_exit_factor( i ) for i in range(0, self.layers_n) ]))
+	def normalized_per_layer_loss_scale( self, l, t=None ):
+		return (self.cirriculum(l, t) * self.early_exit_factor( l )) / sum([ self.cirriculum(i, t) * self.early_exit_factor( i ) for i in range(0, self.layers_n) ])
 
 	def early_exit_factor( self, l ):
 		if 0 <= l and l < self.layers_n:
diff --git a/vall_e/models/base.py b/vall_e/models/base.py
index d2ea128..09cea8c 100755
--- a/vall_e/models/base.py
+++ b/vall_e/models/base.py
@@ -472,6 +472,7 @@ class Base(nn.Module):
 		self.unified_position_ids = unified_position_ids
 		self.interleave = interleave
 		self.layerskip = layerskip
+		self.training_step = 0
 
 		self.text_emb = Embedding(n_text_tokens, d_model)
 		self.langs_emb = None
@@ -1506,12 +1507,13 @@ class Base(nn.Module):
 						self.stats[k].append( v )
 
 				for k, v in self.loss.items():
-					self.loss[k] = self.model.early_exit_loss( losses=v )
+					self.loss[k] = self.model.early_exit_loss( losses=v, t=self.training_step )
+				# ick
+				self.training_step += 1
 
 				for k, v in self.stats.items():
 					self.stats[k] = sum( v ) / len( v )
 
-
 			# include any additional losses (for example: MoE router)
 			if output.aux_loss is not None:
 				loss["aux_loss"] = output.aux_loss