diff --git a/doc/introduction.md b/doc/introduction.md
index d1900b9b4..19fbbebd8 100644
--- a/doc/introduction.md
+++ b/doc/introduction.md
@@ -47,3 +47,8 @@ estimate of pi (waiting until the computation has finished if necessary).
 - [Tutorial](tutorial.md)
 - [About the System](about-the-system.md)
 - [Using Ray on a Cluster](using-ray-on-a-cluster.md)
+
+## Example Applications
+
+- [Hyperparameter Optimization](../examples/hyperopt/README.md)
+- [Batch L-BFGS](../examples/lbfgs/README.md)
diff --git a/examples/hyperopt/README.md b/examples/hyperopt/README.md
index 654ea8f00..d13d11a0e 100644
--- a/examples/hyperopt/README.md
+++ b/examples/hyperopt/README.md
@@ -5,9 +5,10 @@ To run the application, first install this dependency.
 
 - [TensorFlow](https://www.tensorflow.org/)
 
-Then run the following.
+Then from the directory `ray/examples/hyperopt/` run the following.
 
 ```
+source ../../setup-env.sh
 python driver.py
 ```
 
diff --git a/examples/lbfgs/README.md b/examples/lbfgs/README.md
new file mode 100644
index 000000000..0e30f20cb
--- /dev/null
+++ b/examples/lbfgs/README.md
@@ -0,0 +1,165 @@
+## Batch L-BFGS
+
+This document provides a walkthrough of the L-BFGS example. To run the
+application, first install these dependencies.
+
+- SciPy
+- [TensorFlow](https://www.tensorflow.org/).
+
+Then from the directory `ray/examples/lbfgs/` run the following.
+
+```
+source ../../setup-env.sh
+python driver.py
+```
+
+Optimization is at the heart of many machine learning algorithms. Much of
+machine learning involves specifying a loss function and finding the parameters
+that minimize the loss. If we can compute the gradient of the loss function,
+then we can apply a variety of gradient-based optimization algorithms. L-BFGS is
+one such algorithm.
+
+### The serial version
+
+First we load the data in batches. Here, each element in `batches` is a tuple
+whose first component is a batch of `100` images and whose second component is a
+batch of the `100` corresponding labels. For simplicity, we use TensorFlow's
+built in methods for loading the data.
+
+```python
+from tensorflow.examples.tutorials.mnist import input_data
+mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
+
+batch_size = 100
+num_batches = mnist.train.num_examples / batch_size
+batches = [mnist.train.next_batch(batch_size) for _ in range(num_batches)]
+```
+
+Now, suppose we have defined a function which takes a set of model parameters
+`theta` and a batch of data (both images and labels) and computes the loss for
+that choice of model parameters on that batch of data. Similarly, suppose we've
+also defined a function that takes the same arguments and computes the gradient
+of the loss for that choice of model parameters.
+
+```python
+def loss(theta, xs, ys):
+  # compute the loss on a batch of data
+  return loss
+
+def grad(theta, xs, ys):
+  # compute the gradient on a batch of data
+  return grad
+
+def full_loss(theta):
+  # compute the loss on the full data set
+  return sum([loss(theta, xs, ys) for (xs, ys) in batches])
+
+def full_grad(theta):
+  # compute the gradient on the full data set
+  return sum([grad(theta, xs, ys) for (xs, ys) in batches])
+```
+
+Since we are working with a small dataset, we don't actually need to separate
+these methods into the part that operates on a batch and the part that operates
+on the full dataset, but doing so will make the distributed version clearer.
+
+Now, if we wish to optimize the loss function using L-BFGS, we simply plug these
+functions, along with an initial choice of model parameters, into
+`scipy.optimize.fmin_l_bfgs_b`.
+
+```python
+theta_init = np.zeros(functions.dim)
+result = scipy.optimize.fmin_l_bfgs_b(full_loss, theta_init, fprime=full_grad)
+```
+
+### The distributed version
+
+The extent to which this computation can be parallelized depends on the
+specifics of the optimization problem, but for large datasets, the computation
+of the gradient itself is often embarrassingly parallel.
+
+To run this example in Ray, we use three files.
+
+- [driver.py](driver.py) - This is the script that gets run. It launches the
+  remote tasks and retrieves the results. The application can be run with
+  `python driver.py`.
+- [functions.py](functions.py) - This is the file that defines the remote
+  functions (in this case, just `loss` and `grad`).
+- [worker.py](worker.py) - This is the Python code that each worker process
+  runs. It imports the relevant modules and tells the scheduler what functions
+  it knows how to execute. Then it enters a loop that waits to receive tasks
+  from the scheduler.
+
+First, let's turn the data into a collection of remote objects.
+
+```python
+batch_refs = [ray.put(xs), ray.put(ys) for (xs, ys) in batches]
+```
+
+MNIST easily fits on a single machine, but for larger data sets, we will need to
+use remote functions to distribute the loading of the data.
+
+Now, lets turn `loss` and `grad` into remote functions. In the example
+application, this is done in [functions.py](functions.py).
+
+```python
+@ray.remote([np.ndarray, np.ndarray, np.ndarray], [float])
+def loss(theta, xs, ys):
+  # compute the loss
+  return loss
+
+@ray.remote([np.ndarray, np.ndarray, np.ndarray], [np.ndarray])
+def grad(theta, xs, ys):
+  # compute the gradient
+  return grad
+```
+
+The only difference is that we added the `@ray.remote` decorator specifying a
+little bit of type information (the inputs consist of numpy arrays, `loss`
+returns a float, and `grad` returns a numpy array).
+
+Now, it is easy to speed up the computation of the full loss and the full
+gradient.
+
+```python
+def full_loss(theta):
+  theta_ref = ray.put(theta)
+  loss_refs = [loss(theta_ref, xs_ref, ys_ref) for (xs_ref, ys_ref) in batch_refs]
+  return sum([ray.get(loss_ref) for loss_ref in loss_refs])
+
+def full_grad(theta):
+  theta_ref = ray.put(theta)
+  grad_refs = [grad(theta_ref, xs_ref, ys_ref) for (xs_ref, ys_ref) in batch_refs]
+  return sum([ray.get(grad_ref) for grad_ref in grad_refs]).astype("float64") # This conversion is necessary for use with fmin_l_bfgs_b.
+```
+
+Note that we turn `theta` into a remote object with the line `theta_ref =
+ray.put(theta)` before passing it into the remote functions. If we had written
+
+```python
+[loss(theta, xs_ref, ys_ref) for ... in batch_refs]
+```
+
+instead of
+
+```python
+theta_ref = ray.put(theta)
+[loss(theta_ref, xs_ref, ys_ref) for ... in batch_refs]
+```
+
+then each task that got sent to the scheduler (one for every element of
+`batch_refs`) would have had a copy of `theta` serialized inside of it. Since
+`theta` here consists of the parameters of a potentially large model, this is
+inefficient. *Large objects should be passed by object reference to remote
+functions and not by value*.
+
+We use remote functions and remote objects internally in the implementation of
+`full_loss` and `full_grad`, but the user-facing behavior of these methods is
+identical to the behavior in the serial version.
+
+We can now optimize the objective with the same function call as before.
+
+```python
+theta_init = np.zeros(functions.dim)
+result = scipy.optimize.fmin_l_bfgs_b(full_loss, theta_init, fprime=full_grad)
+```
diff --git a/examples/lbfgs/driver.py b/examples/lbfgs/driver.py
index 37186e87a..7f4b307cb 100644
--- a/examples/lbfgs/driver.py
+++ b/examples/lbfgs/driver.py
@@ -1,29 +1,24 @@
 import numpy as np
 import scipy.optimize
 import os
-import time
 import ray
-import ray.services as services
-import ray.worker as worker
-
-import ray.array.remote as ra
-import ray.array.distributed as da
 
 import functions
 
 from tensorflow.examples.tutorials.mnist import input_data
-mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
-
-batch_size = 100
-num_batches = mnist.train.num_examples / batch_size
-batches = [mnist.train.next_batch(batch_size) for _ in range(num_batches)]
 
 if __name__ == "__main__":
   worker_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "worker.py")
-  services.start_ray_local(num_workers=16, worker_path=worker_path)
+  ray.services.start_ray_local(num_workers=16, worker_path=worker_path)
 
-  x_batches = [ray.put(batches[i][0]) for i in range(num_batches)]
-  y_batches = [ray.put(batches[i][1]) for i in range(num_batches)]
+  print "Downloading and loading MNIST data..."
+  mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
+
+  batch_size = 100
+  num_batches = mnist.train.num_examples / batch_size
+  batches = [mnist.train.next_batch(batch_size) for _ in range(num_batches)]
+
+  batch_refs = [(ray.put(xs), ray.put(ys)) for (xs, ys) in batches]
 
   # From the perspective of scipy.optimize.fmin_l_bfgs_b, full_loss is simply a
   # function which takes some parameters theta, and computes a loss. Similarly,
@@ -36,17 +31,13 @@ if __name__ == "__main__":
   # algorithm.
   def full_loss(theta):
     theta_ref = ray.put(theta)
-    val_ref = ra.sum_list(*[functions.loss(theta_ref, x_batches[i], y_batches[i]) for i in range(num_batches)])
-    return ray.get(val_ref)
+    loss_refs = [functions.loss(theta_ref, xs_ref, ys_ref) for (xs_ref, ys_ref) in batch_refs]
+    return sum([ray.get(loss_ref) for loss_ref in loss_refs])
 
   def full_grad(theta):
     theta_ref = ray.put(theta)
-    grad_ref = ra.sum_list(*[functions.grad(theta_ref, x_batches[i], y_batches[i]) for i in range(num_batches)])
-    return ray.get(grad_ref).astype("float64") # This conversion is necessary for use with fmin_l_bfgs_b.
+    grad_refs = [functions.grad(theta_ref, xs_ref, ys_ref) for (xs_ref, ys_ref) in batch_refs]
+    return sum([ray.get(grad_ref) for grad_ref in grad_refs]).astype("float64") # This conversion is necessary for use with fmin_l_bfgs_b.
 
   theta_init = np.zeros(functions.dim)
-
-  start_time = time.time()
   result = scipy.optimize.fmin_l_bfgs_b(full_loss, theta_init, maxiter=10, fprime=full_grad, disp=True)
-  end_time = time.time()
-  print "Elapsed time = {}".format(end_time - start_time)
diff --git a/examples/lbfgs/functions.py b/examples/lbfgs/functions.py
index e20078b7a..eece0f9db 100644
--- a/examples/lbfgs/functions.py
+++ b/examples/lbfgs/functions.py
@@ -1,6 +1,6 @@
-import numpy as np
 import ray
 
+import numpy as np
 import tensorflow as tf
 
 image_dimension = 784