diff --git a/benchmarl/run.py b/benchmarl/run.py
index 2328653e..8ec2addf 100644
--- a/benchmarl/run.py
+++ b/benchmarl/run.py
@@ -9,19 +9,15 @@
 def hydra_experiment(cfg: DictConfig) -> None:
     hydra_choices = HydraConfig.get().runtime.choices
     task_name = hydra_choices.task
-    print(f"\nAlgorithm: {hydra_choices.algorithm}, Task: {task_name}")
+    algorithm_name = hydra_choices.algorithm
+
+    print(f"\nAlgorithm: {algorithm_name}, Task: {task_name}")
     print("\nLoaded config:\n")
     print(OmegaConf.to_yaml(cfg))
 
-    experiment = load_experiment_from_hydra(
-        cfg,
-        task_name=task_name,
-    )
+    experiment = load_experiment_from_hydra(cfg, task_name=task_name)
     experiment.run()
 
 
 if __name__ == "__main__":
     hydra_experiment()
-
-    # You can run multiple experiments like so
-    # python simple_hydra_run.py --multirun algorithm=mappo,qmix,maddpg,masac task=vmas/balance
diff --git a/examples/vmas_run.py b/examples/vmas_run.py
deleted file mode 100644
index 60059e1d..00000000
--- a/examples/vmas_run.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import hydra
-from benchmarl.experiment import Experiment
-
-from benchmarl.hydra_config import (
-    load_algorithm_config_from_hydra,
-    load_experiment_config_from_hydra,
-    load_model_config_from_hydra,
-    load_task_config_from_hydra,
-)
-from hydra.core.hydra_config import HydraConfig
-from omegaconf import DictConfig, OmegaConf
-
-
-@hydra.main(version_base=None, config_path="../benchmarl/conf", config_name="config")
-def hydra_experiment(cfg: DictConfig) -> None:
-    hydra_choices = HydraConfig.get().runtime.choices
-    task_name = hydra_choices.task
-    print(f"\nAlgorithm: {hydra_choices.algorithm}, Task: {task_name}")
-
-    algorithm_config = load_algorithm_config_from_hydra(cfg.algorithm)
-    task_config = load_task_config_from_hydra(cfg.task, task_name)
-    model_config = load_model_config_from_hydra(cfg.model)
-    experiment_config = cfg.experiment
-
-    # Hyperparameter changes for VMAS experiments
-    experiment_config.sampling_device = "cuda"
-    experiment_config.train_device = "cuda"
-    experiment_config.collected_frames_per_batch = 60_000
-    experiment_config.n_envs_per_worker = 600
-    experiment_config.on_policy_minibatch_size = 4096
-    experiment_config.evaluation_episodes = 200
-    experiment_config = load_experiment_config_from_hydra(cfg.experiment)
-
-    print("\nLoaded config:\n")
-    print(OmegaConf.to_yaml(cfg))
-
-    experiment = Experiment(
-        task=task_config,
-        algorithm_config=algorithm_config,
-        model_config=model_config,
-        seed=cfg.seed,
-        config=experiment_config,
-    )
-    experiment.run()
-
-
-if __name__ == "__main__":
-    hydra_experiment()
-    # To reproduce the VMAS results launch this with
-    # python run.py algorithm=ippo "task=vmas/navigation,vmas/balance,vmas/sampling" "seed=0,1,2"
diff --git a/premade_scripts/vmas/config.yaml b/premade_scripts/vmas/config.yaml
new file mode 100644
index 00000000..9fc22595
--- /dev/null
+++ b/premade_scripts/vmas/config.yaml
@@ -0,0 +1,22 @@
+defaults:
+  - experiment: base_experiment
+  - algorithm: mappo
+  - task: vmas/balance
+  - model: layers/mlp
+  - model@critic_model: layers/mlp
+  - _self_
+
+seed: 0
+
+hydra:
+  searchpath:
+   # Tells hydra to add the default benchmarl configuration to its path
+    - pkg://benchmarl/conf
+
+experiment:
+    sampling_device: "cuda"
+    train_device: "cuda"
+    collected_frames_per_batch: 60_000
+    n_envs_per_worker: 600
+    on_policy_minibatch_size: 4096
+    evaluation_episodes: 200
diff --git a/premade_scripts/vmas/vmas_run.py b/premade_scripts/vmas/vmas_run.py
new file mode 100644
index 00000000..3f0d9c21
--- /dev/null
+++ b/premade_scripts/vmas/vmas_run.py
@@ -0,0 +1,24 @@
+import hydra
+from benchmarl.experiment import Experiment
+
+from benchmarl.hydra_config import load_experiment_from_hydra
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import DictConfig, OmegaConf
+
+
+@hydra.main(version_base=None, config_path=".", config_name="config")
+def hydra_experiment(cfg: DictConfig) -> None:
+    hydra_choices = HydraConfig.get().runtime.choices
+    task_name = hydra_choices.task
+    algorithm_name = hydra_choices.algorithm
+
+    print(f"\nAlgorithm: {algorithm_name}, Task: {task_name}")
+    print("\nLoaded config:\n")
+    print(OmegaConf.to_yaml(cfg))
+
+    experiment: Experiment = load_experiment_from_hydra(cfg, task_name=task_name)
+    experiment.run()
+
+
+if __name__ == "__main__":
+    hydra_experiment()