Fix documentation.

examples/hyperopt/README.md

@@ -88,9 +88,7 @@ def train_cnn_and_compute_accuracy(hyperparameters, train_images, train_labels,
   return validation_accuracy
 ```
 
-The only difference is that we added the `@ray.remote` decorator specifying a
-little bit of type information (the input is a dictionary along with some numpy
-arrays, and the return value is a float).
+The only difference is that we added the `@ray.remote` decorator.
 
 Now a call to `train_cnn_and_compute_accuracy` does not execute the function. It
 submits the task to the scheduler and returns an object ID for the output

examples/lbfgs/README.md

@@ -102,9 +102,7 @@ def grad(theta, xs, ys):
   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).
+The only difference is that we added the `@ray.remote` decorator.
 
 Now, it is easy to speed up the computation of the full loss and the full
 gradient.