diff --git a/src/refiners/training_utils/__init__.py b/src/refiners/training_utils/__init__.py
index 8850b34..8913ea1 100644
--- a/src/refiners/training_utils/__init__.py
+++ b/src/refiners/training_utils/__init__.py
@@ -8,11 +8,11 @@ from refiners.training_utils.callback import Callback, CallbackConfig
 from refiners.training_utils.clock import ClockConfig
 from refiners.training_utils.config import (
     BaseConfig,
+    LRSchedulerConfig,
+    LRSchedulerType,
     ModelConfig,
     OptimizerConfig,
     Optimizers,
-    SchedulerConfig,
-    SchedulerType,
     TrainingConfig,
 )
 from refiners.training_utils.gradient_clipping import GradientClippingConfig
@@ -48,11 +48,11 @@ __all__ = [
     "CallbackConfig",
     "WandbMixin",
     "WandbConfig",
-    "SchedulerConfig",
+    "LRSchedulerConfig",
     "OptimizerConfig",
     "TrainingConfig",
     "ClockConfig",
     "GradientClippingConfig",
     "Optimizers",
-    "SchedulerType",
+    "LRSchedulerType",
 ]
diff --git a/src/refiners/training_utils/config.py b/src/refiners/training_utils/config.py
index 91e41e2..4098ea0 100644
--- a/src/refiners/training_utils/config.py
+++ b/src/refiners/training_utils/config.py
@@ -46,7 +46,7 @@ class Optimizers(str, Enum):
     Prodigy = "Prodigy"
 
 
-class SchedulerType(str, Enum):
+class LRSchedulerType(str, Enum):
     STEP_LR = "StepLR"
     EXPONENTIAL_LR = "ExponentialLR"
     REDUCE_LR_ON_PLATEAU = "ReduceLROnPlateau"
@@ -61,8 +61,8 @@ class SchedulerType(str, Enum):
     DEFAULT = "ConstantLR"
 
 
-class SchedulerConfig(BaseModel):
-    scheduler_type: SchedulerType = SchedulerType.DEFAULT
+class LRSchedulerConfig(BaseModel):
+    type: LRSchedulerType = LRSchedulerType.DEFAULT
     update_interval: TimeValue = {"number": 1, "unit": TimeUnit.ITERATION}
     warmup: TimeValue = {"number": 0, "unit": TimeUnit.ITERATION}
     gamma: float = 0.1
@@ -165,7 +165,7 @@ T = TypeVar("T", bound="BaseConfig")
 class BaseConfig(BaseModel):
     training: TrainingConfig
     optimizer: OptimizerConfig
-    scheduler: SchedulerConfig
+    lr_scheduler: LRSchedulerConfig
     clock: ClockConfig = ClockConfig()
     gradient_clipping: GradientClippingConfig = GradientClippingConfig()
 
diff --git a/src/refiners/training_utils/trainer.py b/src/refiners/training_utils/trainer.py
index eb6c25a..c68a08e 100644
--- a/src/refiners/training_utils/trainer.py
+++ b/src/refiners/training_utils/trainer.py
@@ -36,7 +36,7 @@ from refiners.training_utils.common import (
     human_readable_number,
     scoped_seed,
 )
-from refiners.training_utils.config import BaseConfig, ModelConfig, SchedulerType
+from refiners.training_utils.config import BaseConfig, LRSchedulerType, ModelConfig
 from refiners.training_utils.gradient_clipping import GradientClipping, GradientClippingConfig
 
 
@@ -154,7 +154,7 @@ class Trainer(Generic[ConfigType, Batch], ABC):
             training_duration=self.config.training.duration,
             evaluation_interval=self.config.training.evaluation_interval,
             gradient_accumulation=self.config.training.gradient_accumulation,
-            lr_scheduler_interval=self.config.scheduler.update_interval,
+            lr_scheduler_interval=self.config.lr_scheduler.update_interval,
             verbose=config.verbose,
         )
 
@@ -237,21 +237,21 @@ class Trainer(Generic[ConfigType, Batch], ABC):
 
     @cached_property
     def lr_scheduler(self) -> LRScheduler:
-        config = self.config.scheduler
+        config = self.config.lr_scheduler
         scheduler_step_size = config.update_interval["number"]
 
-        match config.scheduler_type:
-            case SchedulerType.CONSTANT_LR:
+        match config.type:
+            case LRSchedulerType.CONSTANT_LR:
                 lr_scheduler = LambdaLR(optimizer=self.optimizer, lr_lambda=lambda _: 1.0)
-            case SchedulerType.STEP_LR:
+            case LRSchedulerType.STEP_LR:
                 lr_scheduler = StepLR(optimizer=self.optimizer, step_size=scheduler_step_size, gamma=config.gamma)
-            case SchedulerType.EXPONENTIAL_LR:
+            case LRSchedulerType.EXPONENTIAL_LR:
                 lr_scheduler = ExponentialLR(optimizer=self.optimizer, gamma=config.gamma)
-            case SchedulerType.COSINE_ANNEALING_LR:
+            case LRSchedulerType.COSINE_ANNEALING_LR:
                 lr_scheduler = CosineAnnealingLR(
                     optimizer=self.optimizer, T_max=scheduler_step_size, eta_min=config.eta_min
                 )
-            case SchedulerType.REDUCE_LR_ON_PLATEAU:
+            case LRSchedulerType.REDUCE_LR_ON_PLATEAU:
                 lr_scheduler = cast(
                     LRScheduler,
                     ReduceLROnPlateau(
@@ -264,24 +264,24 @@ class Trainer(Generic[ConfigType, Batch], ABC):
                         min_lr=config.min_lr,
                     ),
                 )
-            case SchedulerType.LAMBDA_LR:
+            case LRSchedulerType.LAMBDA_LR:
                 assert config.lr_lambda is not None, "lr_lambda must be specified to use LambdaLR"
                 lr_scheduler = LambdaLR(optimizer=self.optimizer, lr_lambda=config.lr_lambda)
-            case SchedulerType.ONE_CYCLE_LR:
+            case LRSchedulerType.ONE_CYCLE_LR:
                 lr_scheduler = OneCycleLR(
                     optimizer=self.optimizer, max_lr=config.max_lr, total_steps=scheduler_step_size
                 )
-            case SchedulerType.MULTIPLICATIVE_LR:
+            case LRSchedulerType.MULTIPLICATIVE_LR:
                 assert config.lr_lambda is not None, "lr_lambda must be specified to use MultiplicativeLR"
                 lr_scheduler = MultiplicativeLR(optimizer=self.optimizer, lr_lambda=config.lr_lambda)
-            case SchedulerType.COSINE_ANNEALING_WARM_RESTARTS:
+            case LRSchedulerType.COSINE_ANNEALING_WARM_RESTARTS:
                 lr_scheduler = CosineAnnealingWarmRestarts(optimizer=self.optimizer, T_0=scheduler_step_size)
-            case SchedulerType.CYCLIC_LR:
+            case LRSchedulerType.CYCLIC_LR:
                 lr_scheduler = CyclicLR(optimizer=self.optimizer, base_lr=config.base_lr, max_lr=config.max_lr)
-            case SchedulerType.MULTI_STEP_LR:
+            case LRSchedulerType.MULTI_STEP_LR:
                 lr_scheduler = MultiStepLR(optimizer=self.optimizer, milestones=config.milestones, gamma=config.gamma)
             case _:
-                raise ValueError(f"Unknown scheduler type: {config.scheduler_type}")
+                raise ValueError(f"Unknown scheduler type: {config.type}")
 
         warmup_scheduler_steps = self.clock.convert_time_value(config.warmup, config.update_interval["unit"])
         if warmup_scheduler_steps > 0:
diff --git a/tests/training_utils/mock_config.toml b/tests/training_utils/mock_config.toml
index a5017a9..bfc8b2d 100644
--- a/tests/training_utils/mock_config.toml
+++ b/tests/training_utils/mock_config.toml
@@ -22,7 +22,7 @@ evaluation_seed = 1
 optimizer = "SGD"
 learning_rate = 1
 
-[scheduler]
-scheduler_type = "ConstantLR"
+[lr_scheduler]
+type = "ConstantLR"
 update_interval = "1:step"
 warmup = "20:step"
diff --git a/tests/training_utils/mock_config_2_models.toml b/tests/training_utils/mock_config_2_models.toml
index 474551f..9980a6f 100644
--- a/tests/training_utils/mock_config_2_models.toml
+++ b/tests/training_utils/mock_config_2_models.toml
@@ -23,6 +23,6 @@ evaluation_seed = 1
 optimizer = "SGD"
 learning_rate = 1
 
-[scheduler]
-scheduler_type = "ConstantLR"
+[lr_scheduler]
+type = "ConstantLR"
 update_interval = "1:step"