diff --git a/recipes/knowledge_distillation_distributed.py b/recipes/knowledge_distillation_distributed.py
index c920f4b069..e287f4b2de 100644
--- a/recipes/knowledge_distillation_distributed.py
+++ b/recipes/knowledge_distillation_distributed.py
@@ -315,7 +315,7 @@ def setup(self, cfg: DictConfig) -> None:
         # Learning rate scheduler can only be set up after number of steps
         # has been computed
         self._lr_scheduler = self._setup_lr_scheduler(
-            cfg_lr_scheduler=cfg.lr_scheduler,
+            cfg_lr_scheduler=cfg.get("lr_scheduler", None),
             num_training_steps=self.total_epochs * self._steps_per_epoch,
             last_epoch=self.global_step - 1,
         )
@@ -626,10 +626,15 @@ def _setup_optimizer(
 
     def _setup_lr_scheduler(
         self,
-        cfg_lr_scheduler: DictConfig,
+        cfg_lr_scheduler: Optional[DictConfig],
         num_training_steps: int,
         last_epoch: int,
-    ) -> Optimizer:
+    ) -> Optional[Optimizer]:
+        if cfg_lr_scheduler is None:
+            log.info(
+                "No learning rate scheduler configured. Using constant learning rate."
+            )
+            return None
         lr_scheduler = config.instantiate(
             cfg_lr_scheduler,
             self._optimizer,
@@ -886,7 +891,8 @@ def train(self) -> None:
                     kd_loss_to_log = running_kd_loss.item() / num_tokens
                     self._optimizer.step()
                     self._optimizer.zero_grad(set_to_none=True)
-                    self._lr_scheduler.step()
+                    if self._lr_scheduler is not None:
+                        self._lr_scheduler.step()
                     # Update the number of steps when the weights are updated
                     self.global_step += 1
 
diff --git a/recipes/knowledge_distillation_single_device.py b/recipes/knowledge_distillation_single_device.py
index ef238da44d..844d7a6d89 100644
--- a/recipes/knowledge_distillation_single_device.py
+++ b/recipes/knowledge_distillation_single_device.py
@@ -306,7 +306,7 @@ def setup(self, cfg: DictConfig) -> None:
         # Learning rate scheduler can only be set up after number of steps
         # has been computed
         self._lr_scheduler = self._setup_lr_scheduler(
-            cfg_lr_scheduler=cfg.lr_scheduler,
+            cfg_lr_scheduler=cfg.get("lr_scheduler", None),
             num_training_steps=self.total_epochs * self._steps_per_epoch,
             last_epoch=self.global_step - 1,
         )
@@ -495,10 +495,16 @@ def _setup_optimizer(
 
     def _setup_lr_scheduler(
         self,
-        cfg_lr_scheduler: DictConfig,
+        cfg_lr_scheduler: Optional[DictConfig],
         num_training_steps: int,
         last_epoch: int,
-    ) -> Optimizer:
+    ) -> Optional[Optimizer]:
+        if cfg_lr_scheduler is None:
+            log.info(
+                "No learning rate scheduler configured. Using constant learning rate."
+            )
+            return None
+
         lr_scheduler = config.instantiate(
             cfg_lr_scheduler,
             self._optimizer,
@@ -727,7 +733,8 @@ def train(self) -> None:
                             )
                         self._optimizer.step()
                         self._optimizer.zero_grad(set_to_none=True)
-                        self._lr_scheduler.step()
+                        if self._lr_scheduler is not None:
+                            self._lr_scheduler.step()
                         # Update the number of steps when the weights are updated
                         self.global_step += 1
 
diff --git a/recipes/lora_dpo_distributed.py b/recipes/lora_dpo_distributed.py
index ab37623cc1..ba2146ffbe 100644
--- a/recipes/lora_dpo_distributed.py
+++ b/recipes/lora_dpo_distributed.py
@@ -284,7 +284,7 @@ def setup(self, cfg: DictConfig) -> None:
         # Learning rate scheduler can only be set up after number of steps
         # has been computed
         self._lr_scheduler = self._setup_lr_scheduler(
-            cfg_lr_scheduler=cfg.lr_scheduler,
+            cfg_lr_scheduler=cfg.get("lr_scheduler", None),
             num_training_steps=self.total_epochs * self._steps_per_epoch,
             last_epoch=self.global_step - 1,
         )
@@ -426,10 +426,16 @@ def _setup_optimizer(
 
     def _setup_lr_scheduler(
         self,
-        cfg_lr_scheduler: DictConfig,
+        cfg_lr_scheduler: Optional[DictConfig],
         num_training_steps: int,
         last_epoch: int,
-    ) -> Optimizer:
+    ) -> Optional[Optimizer]:
+        if cfg_lr_scheduler is None:
+            log.info(
+                "No learning rate scheduler configured. Using constant learning rate."
+            )
+            return None
+
         lr_scheduler = config.instantiate(
             cfg_lr_scheduler,
             self._optimizer,
@@ -679,7 +685,8 @@ def train(self) -> None:
                 if (idx + 1) % self._gradient_accumulation_steps == 0:
                     self._optimizer.step()
                     self._optimizer.zero_grad(set_to_none=True)
-                    self._lr_scheduler.step()
+                    if self._lr_scheduler is not None:
+                        self._lr_scheduler.step()
 
                     # Update the number of steps when the weights are updated
                     self.global_step += 1
diff --git a/recipes/lora_finetune_distributed.py b/recipes/lora_finetune_distributed.py
index 45209814a0..7a9eb97bee 100644
--- a/recipes/lora_finetune_distributed.py
+++ b/recipes/lora_finetune_distributed.py
@@ -334,7 +334,7 @@ def setup(self, cfg: DictConfig) -> None:
         # Learning rate scheduler can only be set up after number of steps
         # has been computed
         self._lr_scheduler = self._setup_lr_scheduler(
-            cfg_lr_scheduler=cfg.lr_scheduler,
+            cfg_lr_scheduler=cfg.get("lr_scheduler", None),
             num_training_steps=self.total_epochs * self._steps_per_epoch,
             last_epoch=self.global_step - 1,
         )
@@ -563,10 +563,16 @@ def _setup_optimizer(
 
     def _setup_lr_scheduler(
         self,
-        cfg_lr_scheduler: DictConfig,
+        cfg_lr_scheduler: Optional[DictConfig],
         num_training_steps: int,
         last_epoch: int,
-    ) -> Optimizer:
+    ) -> Optional[Optimizer]:
+        if cfg_lr_scheduler is None:
+            log.info(
+                "No learning rate scheduler configured. Using constant learning rate."
+            )
+            return None
+
         lr_scheduler = config.instantiate(
             cfg_lr_scheduler,
             self._optimizer,
@@ -837,7 +843,8 @@ def train(self) -> None:
                         )
                     self._optimizer.step()
                     self._optimizer.zero_grad(set_to_none=True)
-                    self._lr_scheduler.step()
+                    if self._lr_scheduler is not None:
+                        self._lr_scheduler.step()
 
                     # Update the number of steps when the weights are updated
                     self.global_step += 1
diff --git a/recipes/lora_finetune_single_device.py b/recipes/lora_finetune_single_device.py
index fcdb3e4ea5..5e7aaa6c16 100644
--- a/recipes/lora_finetune_single_device.py
+++ b/recipes/lora_finetune_single_device.py
@@ -331,7 +331,7 @@ def setup(self, cfg: DictConfig) -> None:
         # Learning rate scheduler can only be set up after number of steps
         # has been computed
         self._lr_scheduler = self._setup_lr_scheduler(
-            cfg_lr_scheduler=cfg.lr_scheduler,
+            cfg_lr_scheduler=cfg.get("lr_scheduler", None),
             num_training_steps=self.total_epochs * self._steps_per_epoch,
             last_epoch=self.global_step - 1,
         )
@@ -497,10 +497,16 @@ def _setup_optimizer(
 
     def _setup_lr_scheduler(
         self,
-        cfg_lr_scheduler: DictConfig,
+        cfg_lr_scheduler: Optional[DictConfig],
         num_training_steps: int,
         last_epoch: int,
-    ) -> Optimizer:
+    ) -> Optional[Optimizer]:
+        if cfg_lr_scheduler is None:
+            log.info(
+                "No learning rate scheduler configured. Using constant learning rate."
+            )
+            return None
+
         lr_scheduler = config.instantiate(
             cfg_lr_scheduler,
             self._optimizer,
@@ -717,7 +723,8 @@ def train(self) -> None:
                             )
                         self._optimizer.step()
                         self._optimizer.zero_grad(set_to_none=True)
-                        self._lr_scheduler.step()
+                        if self._lr_scheduler is not None:
+                            self._lr_scheduler.step()
                         # Update the number of steps when the weights are updated
                         self.global_step += 1