diff --git a/src/lm_saes/config.py b/src/lm_saes/config.py
index e31212c..a7fdcf3 100644
--- a/src/lm_saes/config.py
+++ b/src/lm_saes/config.py
@@ -185,7 +185,7 @@ class SAEConfig(BaseModelConfig):
     init_encoder_with_decoder_transpose: bool = True
 
     l1_coefficient: float = 0.00008
-    l1_coefficient_warmup_steps: int = 0
+    l1_coefficient_warmup_steps: int | float = 0.1
     lp: int = 1
 
     use_ghost_grads: bool = False
@@ -286,7 +286,7 @@ class LanguageModelSAETrainingConfig(LanguageModelSAERunnerConfig):
         "constantwithwarmup"  # constant, constantwithwarmup, linearwarmupdecay, cosineannealing, cosineannealingwarmup, exponentialwarmup
     )
     lr_end: Optional[float] = 1 / 32
-    lr_warm_up_steps: int = 5000
+    lr_warm_up_steps: int | float = 0.1
     lr_cool_down_steps: int = 10000
     train_batch_size: int = 4096
     clip_grad_norm: float = 0.0
@@ -331,6 +331,15 @@ def __post_init__(self):
         total_training_steps = self.total_training_tokens // self.effective_batch_size
         print_once(f"Total training steps: {total_training_steps}")
 
+        if self.lr_scheduler_name == "constantwithwarmup" and isinstance(self.lr_warm_up_steps, float):
+            assert 0 <= self.lr_warm_up_steps <= 1.0
+            self.lr_warm_up_steps = int(self.lr_warm_up_steps * total_training_steps)
+            print_once(f"Learning rate warm up steps: {self.lr_warm_up_steps}")
+        if isinstance(self.sae.l1_coefficient_warmup_steps, float):
+            assert 0 <= self.sae.l1_coefficient_warmup_steps <= 1.0
+            self.sae.l1_coefficient_warmup_steps = int(self.sae.l1_coefficient_warmup_steps * total_training_steps)
+            print_once(f"L1 coefficient warm up steps: {self.sae.l1_coefficient_warmup_steps}")
+
 @dataclass(kw_only=True)
 class LanguageModelSAEPruningConfig(LanguageModelSAERunnerConfig):
     """