diff --git a/src/solve.jl b/src/solve.jl
index a3a16d61c..cf4b2fd50 100644
--- a/src/solve.jl
+++ b/src/solve.jl
@@ -40,17 +40,14 @@ The callback function `callback` is a function which is called after every optim
 step. Its signature is:
 
 ```julia
-callback = (state, loss_val, other_args) -> false
+callback = (state, loss_val) -> false
 ```
 
 where `state` is a `OptimizationState` and stores information for the current
 iteration of the solver and `loss_val` is loss/objective value. For more
 information about the fields of the `state` look at the `OptimizationState`
-documentation. The `other_args` can be the extra things returned from the
-optimization `f`. This allows for saving values from the optimization and
-using them for plotting and display without recalculating. The callback should
-return a Boolean value, and the default should be `false`, such that the
-optimization gets stopped if it returns `true`.
+documentation. The callback should return a Boolean value, and the default
+should be `false`, such that the optimization gets stopped if it returns `true`.
 
 ### Callback Example
 
@@ -67,10 +64,11 @@ function loss(u, p)
     sum(abs2, batch .- pred), pred
 end
 
-callback = function (state, l, pred; doplot = false) #callback function to observe training
+callback = function (state, l; doplot = false) #callback function to observe training
     display(l)
     # plot current prediction against data
     if doplot
+        pred = predict(state.u)
         pl = scatter(t, ode_data[1, :], label = "data")
         scatter!(pl, t, pred[1, :], label = "prediction")
         display(plot(pl))