diff --git a/dreem/inference/eval.py b/dreem/inference/eval.py
index 0627aa57..44e4c277 100644
--- a/dreem/inference/eval.py
+++ b/dreem/inference/eval.py
@@ -52,7 +52,7 @@ def run(cfg: DictConfig) -> dict[int, sio.Labels]:
         "persistent_tracking", False
     )
     logger.info(f"Computing the following metrics:")
-    logger.info(model.metrics.test)
+    logger.info(model.metrics["test"])
     model.test_results["save_path"] = eval_cfg.cfg.runner.save_path
     logger.info(f"Saving results to {model.test_results['save_path']}")
 
diff --git a/dreem/models/gtr_runner.py b/dreem/models/gtr_runner.py
index 2a09ec3a..e9fd9f84 100644
--- a/dreem/models/gtr_runner.py
+++ b/dreem/models/gtr_runner.py
@@ -247,7 +247,7 @@ def configure_optimizers(self) -> dict:
                 "scheduler": scheduler,
                 "monitor": "val_loss",
                 "interval": "epoch",
-                "frequency": 10,
+                "frequency": 1,
             },
         }
 
diff --git a/dreem/models/model_utils.py b/dreem/models/model_utils.py
index b886d4fb..c176bd42 100644
--- a/dreem/models/model_utils.py
+++ b/dreem/models/model_utils.py
@@ -150,14 +150,31 @@ def init_scheduler(
     if config is None:
         return None
     scheduler = config.get("name")
+
     if scheduler is None:
         scheduler = "ReduceLROnPlateau"
 
     scheduler_params = {
         param: val for param, val in config.items() if param.lower() != "name"
     }
+
     try:
-        scheduler_class = getattr(torch.optim.lr_scheduler, scheduler)
+        # if a list is provided, apply each one sequentially
+        if isinstance(scheduler, list):
+            schedulers = []
+            milestones = scheduler_params.get("milestones", None)
+            for ix, s in enumerate(scheduler):
+                params = scheduler_params[str(ix)]
+                schedulers.append(
+                    getattr(torch.optim.lr_scheduler, s)(optimizer, **params)
+                )
+            scheduler_class = torch.optim.lr_scheduler.SequentialLR(
+                optimizer, schedulers, milestones
+            )
+            return scheduler_class
+
+        else:
+            scheduler_class = getattr(torch.optim.lr_scheduler, scheduler)
     except AttributeError:
         if scheduler_class is None:
             print(
diff --git a/dreem/training/configs/base.yaml b/dreem/training/configs/base.yaml
index 7779cd13..b5b3b766 100644
--- a/dreem/training/configs/base.yaml
+++ b/dreem/training/configs/base.yaml
@@ -45,6 +45,18 @@ scheduler:
   threshold: 1e-4
   threshold_mode: "rel"
 
+  # scheduler: # providing a list of schedulers will apply each scheduler sequentially
+  # name: ["LinearLR", "LinearLR"]
+  # "0": 
+  #   start_factor: 0.1
+  #   end_factor: 1
+  #   total_iters: 3
+  # "1":
+  #   start_factor: 1
+  #   end_factor: 0.1
+  #   total_iters: 30
+  # milestones: [10]
+
 tracker:
   window_size: 8
   use_vis_feats: true