diff --git a/ocpmodels/models/equiformer_v2/trainers/forces_trainer.py b/ocpmodels/models/equiformer_v2/trainers/forces_trainer.py
index b8bbc3f66..6284a3dac 100755
--- a/ocpmodels/models/equiformer_v2/trainers/forces_trainer.py
+++ b/ocpmodels/models/equiformer_v2/trainers/forces_trainer.py
@@ -146,10 +146,17 @@ def multiply(obj, num):
             scheduler_params = self.config["optim"]["scheduler_params"]
             for k in scheduler_params.keys():
                 if "epochs" in k:
-                    if isinstance(scheduler_params[k], (int, float, list)):
+                    if isinstance(scheduler_params[k], (int, float)):
                         scheduler_params[k] = int(
                             multiply(scheduler_params[k], n_iter_per_epoch)
                         )
+                    elif isinstance(scheduler_params[k], list):
+                        scheduler_params[k] = [
+                            int(x)
+                            for x in multiply(
+                                scheduler_params[k], n_iter_per_epoch
+                            )
+                        ]
             self.scheduler = LRScheduler(self.optimizer, self.config["optim"])
 
         self.clip_grad_norm = self.config["optim"].get("clip_grad_norm")
diff --git a/ocpmodels/models/equiformer_v2/trainers/lr_scheduler.py b/ocpmodels/models/equiformer_v2/trainers/lr_scheduler.py
index 3bf202681..f3926212d 100755
--- a/ocpmodels/models/equiformer_v2/trainers/lr_scheduler.py
+++ b/ocpmodels/models/equiformer_v2/trainers/lr_scheduler.py
@@ -75,7 +75,7 @@ class MultistepLRLambda:
     def __init__(self, scheduler_params) -> None:
         self.warmup_epochs = aii(scheduler_params["warmup_epochs"], int)
         self.lr_warmup_factor = aii(scheduler_params["warmup_factor"], float)
-        self.lr_decay_epochs = aii(scheduler_params["decay_epochs"], int)
+        self.lr_decay_epochs = aii(scheduler_params["decay_epochs"], list)
         self.lr_gamma = aii(scheduler_params["decay_rate"], float)
 
     def __call__(self, current_step: int) -> float: