diff --git a/pina/solver/garom.py b/pina/solver/garom.py
index 2f763a700..b854ce786 100644
--- a/pina/solver/garom.py
+++ b/pina/solver/garom.py
@@ -151,9 +151,9 @@ def _train_generator(self, parameters, snapshots):
         :return: The residual loss and the generator loss.
         :rtype: tuple[torch.Tensor, torch.Tensor]
         """
-        optimizer = self.optimizer_generator
-        optimizer.zero_grad()
+        self.optimizer_generator.instance.zero_grad()
 
+        # Generate a batch of images
         generated_snapshots = self.sample(parameters)
 
         # generator loss
@@ -165,7 +165,8 @@ def _train_generator(self, parameters, snapshots):
 
         # backward step
         g_loss.backward()
-        optimizer.step()
+        self.optimizer_generator.instance.step()
+        self.scheduler_generator.instance.step()
 
         return r_loss, g_loss
 
@@ -196,8 +197,7 @@ def _train_discriminator(self, parameters, snapshots):
         :return: The residual loss and the generator loss.
         :rtype: tuple[torch.Tensor, torch.Tensor]
         """
-        optimizer = self.optimizer_discriminator
-        optimizer.zero_grad()
+        self.optimizer_discriminator.instance.zero_grad()
 
         # Generate a batch of images
         generated_snapshots = self.sample(parameters)
@@ -213,7 +213,8 @@ def _train_discriminator(self, parameters, snapshots):
 
         # backward step
         d_loss.backward()
-        optimizer.step()
+        self.optimizer_discriminator.instance.step()
+        self.scheduler_discriminator.instance.step()
 
         return d_loss_real, d_loss_fake, d_loss
 
@@ -345,7 +346,7 @@ def optimizer_generator(self):
         :return: The optimizer for the generator.
         :rtype: Optimizer
         """
-        return self.optimizers[0].instance
+        return self.optimizers[0]
 
     @property
     def optimizer_discriminator(self):
@@ -355,7 +356,7 @@ def optimizer_discriminator(self):
         :return: The optimizer for the discriminator.
         :rtype: Optimizer
         """
-        return self.optimizers[1].instance
+        return self.optimizers[1]
 
     @property
     def scheduler_generator(self):
@@ -365,7 +366,7 @@ def scheduler_generator(self):
         :return: The scheduler for the generator.
         :rtype: Scheduler
         """
-        return self.schedulers[0].instance
+        return self.schedulers[0]
 
     @property
     def scheduler_discriminator(self):
@@ -375,4 +376,4 @@ def scheduler_discriminator(self):
         :return: The scheduler for the discriminator.
         :rtype: Scheduler
         """
-        return self.schedulers[1].instance
+        return self.schedulers[1]
diff --git a/pina/solver/physics_informed_solver/competitive_pinn.py b/pina/solver/physics_informed_solver/competitive_pinn.py
index 058c53f40..a61755148 100644
--- a/pina/solver/physics_informed_solver/competitive_pinn.py
+++ b/pina/solver/physics_informed_solver/competitive_pinn.py
@@ -130,11 +130,15 @@ def training_step(self, batch):
         loss = super().training_step(batch)
         self.manual_backward(loss)
         self.optimizer_model.instance.step()
+        self.scheduler_model.instance.step()
+
         # train discriminator
         self.optimizer_discriminator.instance.zero_grad()
         loss = super().training_step(batch)
         self.manual_backward(-loss)
         self.optimizer_discriminator.instance.step()
+        self.scheduler_discriminator.instance.step()
+
         return loss
 
     def loss_phys(self, samples, equation):
diff --git a/pina/solver/physics_informed_solver/self_adaptive_pinn.py b/pina/solver/physics_informed_solver/self_adaptive_pinn.py
index a6310d515..1c534b01a 100644
--- a/pina/solver/physics_informed_solver/self_adaptive_pinn.py
+++ b/pina/solver/physics_informed_solver/self_adaptive_pinn.py
@@ -188,12 +188,14 @@ def training_step(self, batch):
         loss = super().training_step(batch)
         self.manual_backward(-loss)
         self.optimizer_weights.instance.step()
+        self.scheduler_weights.instance.step()
 
         # Model optimization
         self.optimizer_model.instance.zero_grad()
         loss = super().training_step(batch)
         self.manual_backward(loss)
         self.optimizer_model.instance.step()
+        self.scheduler_model.instance.step()
 
         return loss