diff --git a/README.rst b/README.rst
index 594127a43..9cdab3ef4 100644
--- a/README.rst
+++ b/README.rst
@@ -87,34 +87,39 @@ To run this example, you will need to install the following:
 
 .. code-block:: bash
 
-    $ pip install ray[tune] torch torchvision filelock
+    $ pip install ray[tune]
 
 
-This example runs a parallel grid search to train a Convolutional Neural Network using PyTorch.
+This example runs a parallel grid search to optimize an example objective function.
 
 .. code-block:: python
 
 
-    import torch.optim as optim
     from ray import tune
-    from ray.tune.examples.mnist_pytorch import (
-        get_data_loaders, ConvNet, train, test)
 
 
-    def train_mnist(config):
-        train_loader, test_loader = get_data_loaders()
-        model = ConvNet()
-        optimizer = optim.SGD(model.parameters(), lr=config["lr"])
-        for i in range(10):
-            train(model, optimizer, train_loader)
-            acc = test(model, test_loader)
-            tune.track.log(mean_accuracy=acc)
+    def objective(step, alpha, beta):
+        return (0.1 + alpha * step / 100)**(-1) + beta * 0.1
+
+
+    def training_function(config):
+        # Hyperparameters
+        alpha, beta = config["alpha"], config["beta"]
+        for step in range(10):
+            # Iterative training function - can be any arbitrary training procedure.
+            intermediate_score = objective(step, alpha, beta)
+            # Feed the score back back to Tune.
+            tune.report(mean_loss=intermediate_score)
 
 
     analysis = tune.run(
-        train_mnist, config={"lr": tune.grid_search([0.001, 0.01, 0.1])})
+        training_function,
+        config={
+            "alpha": tune.grid_search([0.001, 0.01, 0.1]),
+            "beta": tune.choice([1, 2, 3])
+        })
 
-    print("Best config: ", analysis.get_best_config(metric="mean_accuracy"))
+    print("Best config: ", analysis.get_best_config(metric="mean_loss"))
 
     # Get a dataframe for analyzing trial results.
     df = analysis.dataframe()