diff --git a/vall_e/engines/base.py b/vall_e/engines/base.py
index 537a364..1da2016 100755
--- a/vall_e/engines/base.py
+++ b/vall_e/engines/base.py
@@ -76,7 +76,6 @@ class Engine():
 
 		self._frozen_params = set()
 
-		self.max_nan_losses = 8
 		self.loss_scaler = torch.cuda.amp.GradScaler() if cfg.trainer.scale_loss else None
 
 		self.current_batch_size = 0
@@ -294,12 +293,6 @@ class Engine():
 		stats |= {k: v.item() for k, v in losses.items()}
 		stats |= self.gather_attribute("scalar")
 
-		if torch.isnan(loss).any():
-			self.max_nan_losses = self.max_nan_losses - 1
-			if self.max_nan_losses < 0:
-				raise RuntimeError("Too many NaN losses detected.")
-			return stats
-
 		self.backward(loss)
 		self.step()
 
@@ -545,12 +538,16 @@ class Engines(dict[str, Engine]):
 					raise RuntimeError("Out of memory during forward pass!")
 					"""
 
+			# this causes problems in distributed training
+			# it's probably required to do all_reduce for nan checks
+			"""
 			# no results are returned when a nan is encountered, so catch it here too
 			if res is None:
 				engine.max_nan_losses = engine.max_nan_losses - 1
 				if engine.max_nan_losses < 0:
 					raise RuntimeError("Too many NaN losses detected.")
 				continue
+			"""
 			
 			loss, engine_stats = res
 			engine_stats |= self.gather_attribute("scalar")
diff --git a/vall_e/engines/deepspeed.py b/vall_e/engines/deepspeed.py
index 4edc001..f31d500 100755
--- a/vall_e/engines/deepspeed.py
+++ b/vall_e/engines/deepspeed.py
@@ -62,9 +62,7 @@ class Engine(DeepSpeedEngine):
 		self.global_samples = stats["global_samples"]
 		self.tokens_processed = stats["tokens_processed"]
 
-		self.max_nan_losses = 8
 		self.current_batch_size = 0
-		self.skip_on_nan = True
 
 	def freeze(self, freeze_all=True):
 		# freeze non-LoRA params if requested
@@ -151,12 +149,14 @@ class Engine(DeepSpeedEngine):
 		stats |= {k: v.item() for k, v in losses.items()}
 		stats |= self.gather_attribute("scalar")
 
+		"""
 		if torch.isnan(loss).any():
 			self.max_nan_losses = self.max_nan_losses - 1
 			if self.max_nan_losses < 0:
 				raise RuntimeError("Too many NaN losses detected.")
 			
 			return stats
+		"""
 
 		self.backward(loss)
 		self.step()
diff --git a/vall_e/train.py b/vall_e/train.py
index 23ac137..69175ca 100755
--- a/vall_e/train.py
+++ b/vall_e/train.py
@@ -101,9 +101,6 @@ def train_feeder(engine, batch, teacher=None):
 
 		loss = torch.stack([*losses.values()]).sum()
 
-	if torch.isnan(loss).any():
-		return
-
 	stats = {}
 	stats |= {k: v.item() for k, v in losses.items()}
 	stats |= {k: v.item() for k, v in stat.items()}