diff --git a/hyperswap/src/training.py b/hyperswap/src/training.py
index 0e57faf..5621635 100644
--- a/hyperswap/src/training.py
+++ b/hyperswap/src/training.py
@@ -62,10 +62,10 @@ class HyperSwapTrainer(LightningModule):
 		return output_tensor, output_mask
 
 	def configure_optimizers(self) -> Tuple[OptimizerSet, OptimizerSet]:
-		generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = self.config_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4)
-		discriminator_optimizer = torch.optim.AdamW(self.discriminator.parameters(), lr = self.config_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4)
-		generator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(generator_optimizer, T_0 = 300, T_mult = 2)
-		discriminator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(discriminator_optimizer, T_0 = 300, T_mult = 2)
+		generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = self.config_learning_rate, betas = (0.5, 0.999), weight_decay = 1e-4, eps = 1e-8)
+		discriminator_optimizer = torch.optim.AdamW(self.discriminator.parameters(), lr = self.config_learning_rate * 0.5, betas = (0.5, 0.999), weight_decay = 1e-4, eps = 1e-8)
+		generator_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(generator_optimizer, mode = 'min', factor = 0.7, patience = 2000, min_lr = 1e-8)
+		discriminator_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(discriminator_optimizer, mode = 'min', factor = 0.7, patience = 2000, min_lr = 1e-8)
 
 		generator_config =\
 		{
@@ -73,7 +73,7 @@ class HyperSwapTrainer(LightningModule):
 			'lr_scheduler':
 			{
 				'scheduler': generator_scheduler,
-				'interval': 'step'
+				'monitor': 'generator_loss'
 			}
 		}
 		discriminator_config =\
@@ -82,7 +82,7 @@ class HyperSwapTrainer(LightningModule):
 			'lr_scheduler':
 			{
 				'scheduler': discriminator_scheduler,
-				'interval': 'step'
+				'monitor': 'discriminator_loss'
 			}
 		}
 		return generator_config, discriminator_config
@@ -125,7 +125,7 @@ class HyperSwapTrainer(LightningModule):
 				self.clip_gradients(
 					generator_optimizer,
 					gradient_clip_val = self.config_gradient_clip,
-					gradient_clip_algorithm = 'value'
+					gradient_clip_algorithm = 'norm'
 				)
 			generator_optimizer.step()
 			generator_optimizer.zero_grad()
@@ -139,7 +139,7 @@ class HyperSwapTrainer(LightningModule):
 				self.clip_gradients(
 					discriminator_optimizer,
 					gradient_clip_val = self.config_gradient_clip,
-					gradient_clip_algorithm = 'value'
+					gradient_clip_algorithm = 'norm'
 				)
 			discriminator_optimizer.step()
 			discriminator_optimizer.zero_grad()