diff --git a/src/stimulus/learner/raytune_learner.py b/src/stimulus/learner/raytune_learner.py
index e147641..d689661 100644
--- a/src/stimulus/learner/raytune_learner.py
+++ b/src/stimulus/learner/raytune_learner.py
@@ -42,6 +42,7 @@ def __init__(
         tune_run_name: Optional[str] = None,
         *,
         debug: bool = False,
+        autoscaler: bool = False,
     ) -> None:
         """Initialize the TuneWrapper with the paths to the config, model, and data."""
         self.config = config.model_dump()
@@ -94,17 +95,27 @@ def __init__(
         self.config["encoder_loader"] = encoder_loader
         self.config["ray_worker_seed"] = tune.randint(0, 1000)
 
-        self.tuner = self.tuner_initialization()
+        self.gpu_per_trial = config.tune.tune_params.gpu_per_trial
+        self.cpu_per_trial = config.tune.tune_params.cpu_per_trial
 
-    def tuner_initialization(self) -> tune.Tuner:
+        self.tuner = self.tuner_initialization(autoscaler=autoscaler)
+
+    def tuner_initialization(self, *, autoscaler: bool = False) -> tune.Tuner:
         """Prepare the tuner with the configs."""
         # Get available resources from Ray cluster
         cluster_res = cluster_resources()
         logging.info(f"CLUSTER resources   ->  {cluster_res}")
 
         # Check per-trial resources
-        self.gpu_per_trial = self._check_per_trial_resources("gpu_per_trial", cluster_res, "GPU")
-        self.cpu_per_trial = self._check_per_trial_resources("cpu_per_trial", cluster_res, "CPU")
+        if self.gpu_per_trial > cluster_res["GPU"] and not autoscaler:
+            raise ValueError(
+                "GPU per trial is more than what is available in the cluster, set autoscaler to True to allow for autoscaler to be used.",
+            )
+
+        if self.cpu_per_trial > cluster_res["CPU"] and not autoscaler:
+            raise ValueError(
+                "CPU per trial is more than what is available in the cluster, set autoscaler to True to allow for autoscaler to be used.",
+            )
 
         logging.info(f"PER_TRIAL resources ->  GPU: {self.gpu_per_trial} CPU: {self.cpu_per_trial}")
 
@@ -122,70 +133,6 @@ def tune(self) -> None:
         """Run the tuning process."""
         self.tuner.fit()
 
-    def _check_per_trial_resources(
-        self,
-        resource_key: str,
-        cluster_max_resources: dict[str, float],
-        resource_type: str,
-    ) -> float:
-        """Check requested per-trial resources against available cluster resources.
-
-        This function validates and adjusts the requested per-trial resource allocation based on the
-        available cluster resources. It handles three cases:
-        1. Resource request is within cluster limits - uses requested amount
-        2. Resource request exceeds cluster limits - warns and uses maximum available
-        3. No resource request specified - calculates reasonable default based on cluster capacity
-
-        Args:
-            resource_key: Key in config for the resource (e.g. "gpu_per_trial")
-            cluster_max_resources: Dictionary of maximum available cluster resources
-            resource_type: Type of resource being checked ("GPU" or "CPU")
-
-        Returns:
-            float: Number of resources to allocate per trial
-
-        Note:
-            For GPUs, returns 0.0 if no GPUs are available in the cluster.
-        """
-        if resource_type == "GPU" and resource_type not in cluster_max_resources:
-            return 0.0
-
-        per_trial_resource: float = 0.0
-
-        # Check if resource is specified in config and within limits
-        if (
-            resource_key in self.config["tune"]
-            and self.config["tune"][resource_key] <= cluster_max_resources[resource_type]
-        ):
-            per_trial_resource = float(self.config["tune"][resource_key])
-
-        # Warn if requested resources exceed available
-        elif (
-            resource_key in self.config["tune"]
-            and self.config["tune"][resource_key] > cluster_max_resources[resource_type]
-        ):
-            logging.warning(
-                f"\n\n####   WARNING  - {resource_type} per trial are more than what is available. "
-                f"{resource_type} per trial: {self.config['tune'][resource_key]} "
-                f"available: {cluster_max_resources[resource_type]} "
-                "overwriting value to max available",
-            )
-            per_trial_resource = float(cluster_max_resources[resource_type])
-
-        # Set default if not specified
-        elif resource_key not in self.config["tune"]:
-            if cluster_max_resources[resource_type] == 0.0:
-                per_trial_resource = 0.0
-            else:
-                per_trial_resource = float(
-                    max(
-                        1,
-                        (cluster_max_resources[resource_type] // self.config["tune"]["tune_params"]["num_samples"]),
-                    ),
-                )
-
-        return per_trial_resource
-
 
 class TuneModel(Trainable):
     """Trainable model class for Ray Tune."""