diff --git a/tdmpc2/common/scale.py b/tdmpc2/common/scale.py
index a9f4654..8fd1740 100644
--- a/tdmpc2/common/scale.py
+++ b/tdmpc2/common/scale.py
@@ -7,20 +7,16 @@ class RunningScale(torch.nn.Module):
 	def __init__(self, cfg):
 		super().__init__()
 		self.cfg = cfg
-		self._value = Buffer(torch.ones(1, dtype=torch.float32, device=torch.device('cuda')))
+		self.value = Buffer(torch.ones(1, dtype=torch.float32, device=torch.device('cuda')))
 		self._percentiles = Buffer(torch.tensor([5, 95], dtype=torch.float32, device=torch.device('cuda')))
 
 	def state_dict(self):
-		return dict(value=self._value, percentiles=self._percentiles)
+		return dict(value=self.value, percentiles=self._percentiles)
 
 	def load_state_dict(self, state_dict):
-		self._value.copy_(state_dict['value'])
+		self.value.copy_(state_dict['value'])
 		self._percentiles.copy_(state_dict['percentiles'])
 
-	@property
-	def value(self):
-		return self._value
-
 	def _positions(self, x_shape):
 		positions = self._percentiles * (x_shape-1) / 100
 		floored = torch.floor(positions)
@@ -42,7 +38,7 @@ class RunningScale(torch.nn.Module):
 	def update(self, x):
 		percentiles = self._percentile(x.detach())
 		value = torch.clamp(percentiles[1] - percentiles[0], min=1.)
-		self._value.lerp_(value, self.cfg.tau)
+		self.value.data.lerp_(value, self.cfg.tau)
 
 	def forward(self, x, update=False):
 		if update:
diff --git a/tdmpc2/train.py b/tdmpc2/train.py
index 6afb648..48206ec 100755
--- a/tdmpc2/train.py
+++ b/tdmpc2/train.py
@@ -23,6 +23,22 @@ from typing import Any
 from omegaconf import OmegaConf
 torch.backends.cudnn.benchmark = True
 
+torch.set_float32_matmul_precision('high')
+
+def cfg_to_dataclass(cfg, frozen=False):
+	# Converts an OmegaConf config to a dataclass, which will not cause graph breaks
+	cfg_dict = OmegaConf.to_container(cfg)
+	fields = []
+	for key, value in cfg_dict.items():
+		fields.append((key, Any, dataclasses.field(default_factory=lambda value_=value: value_)))
+
+	# Create the dataclass
+	dataclass_name = "Config"
+	dataclass = dataclasses.make_dataclass(dataclass_name, fields, frozen=frozen)
+	def get(self, val, default=None):
+		return getattr(self, val, default)
+	dataclass.get = get
+	return dataclass()
 
 def cfg_to_dataclass(cfg, frozen=False):
 	# Converts an OmegaConf config to a dataclass, which will not cause graph breaks