diff --git a/CHANGELOG.md b/CHANGELOG.md
index 63feff96..061aa6bb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -27,6 +27,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Changed
 
+- Robust restoration of optimizer and scheduler using `ckpt_path`
+  [\#17](https://github.com/mllam/neural-lam/pull/17)
+  @sadamov
+
 - Updated scripts and modules to use `data_config.yaml` instead of `constants.py`
   [\#31](https://github.com/joeloskarsson/neural-lam/pull/31)
   @sadamov
diff --git a/neural_lam/models/ar_model.py b/neural_lam/models/ar_model.py
index 9cda9fc2..29b169d4 100644
--- a/neural_lam/models/ar_model.py
+++ b/neural_lam/models/ar_model.py
@@ -83,8 +83,8 @@ def __init__(self, args):
         if self.output_std:
             self.test_metrics["output_std"] = []  # Treat as metric
 
-        # For making restoring of optimizer state optional (slight hack)
-        self.opt_state = None
+        # For making restoring of optimizer state optional
+        self.restore_opt = args.restore_opt
 
         # For example plotting
         self.n_example_pred = args.n_example_pred
@@ -97,9 +97,6 @@ def configure_optimizers(self):
         opt = torch.optim.AdamW(
             self.parameters(), lr=self.args.lr, betas=(0.9, 0.95)
         )
-        if self.opt_state:
-            opt.load_state_dict(self.opt_state)
-
         return opt
 
     @property
@@ -597,3 +594,6 @@ def on_load_checkpoint(self, checkpoint):
                 )
                 loaded_state_dict[new_key] = loaded_state_dict[old_key]
                 del loaded_state_dict[old_key]
+        if not self.restore_opt:
+            opt = self.configure_optimizers()
+            checkpoint["optimizer_states"] = [opt.state_dict()]
diff --git a/train_model.py b/train_model.py
index df63bcfe..1f348e34 100644
--- a/train_model.py
+++ b/train_model.py
@@ -268,14 +268,7 @@ def main():
 
     # Load model parameters Use new args for model
     model_class = MODELS[args.model]
-    if args.load:
-        model = model_class.load_from_checkpoint(args.load, args=args)
-        if args.restore_opt:
-            # Save for later
-            # Unclear if this works for multi-GPU
-            model.opt_state = torch.load(args.load)["optimizer_states"][0]
-    else:
-        model = model_class(args)
+    model = model_class(args)
 
     prefix = "subset-" if args.subset_ds else ""
     if args.eval:
@@ -330,13 +323,14 @@ def main():
             )
 
         print(f"Running evaluation on {args.eval}")
-        trainer.test(model=model, dataloaders=eval_loader)
+        trainer.test(model=model, dataloaders=eval_loader, ckpt_path=args.load)
     else:
         # Train model
         trainer.fit(
             model=model,
             train_dataloaders=train_loader,
             val_dataloaders=val_loader,
+            ckpt_path=args.load,
         )