diff --git a/src/optimise/train.jl b/src/optimise/train.jl
index 2a2ec5eb..0809e86b 100644
--- a/src/optimise/train.jl
+++ b/src/optimise/train.jl
@@ -1,8 +1,8 @@
 using Juno
 using Flux.Tracker: back!
 
-tocb(f) = f
-tocb(fs::AbstractVector) = () -> foreach(call, fs)
+runall(f) = f
+runall(fs::AbstractVector) = () -> foreach(call, fs)
 
 """
     train!(loss, data, opt; cb = () -> ())
@@ -11,10 +11,11 @@ For each datapoint `d` in `data` computes the gradient of `loss(d...)` through
 backpropagation and calls the optimizer `opt` and the callback `cb`
 (i.e. `opt()` and `cb()`).
 
-Multiple callbacks can be passed to `cb` as an array.
+Multiple optimisers and callbacks can be passed to `opt` and `cb` as arrays.
 """
 function train!(loss, data, opt; cb = () -> ())
-  cb = tocb(cb)
+  cb = runall(cb)
+  opt = runall(opt)
   @progress for d in data
     l = loss(d...)
     isinf(l.data[]) && error("Loss is Inf")