diff --git a/src/adjust.jl b/src/adjust.jl
index 78b3d452..5ff6b7f9 100644
--- a/src/adjust.jl
+++ b/src/adjust.jl
@@ -9,7 +9,7 @@ through training.
 To change just the learning rate, provide a number `η::Real`.
 
 # Example
-```jldoctest
+```jldoctest adjust
 julia> m = (vec = rand(Float32, 2), fun = sin);
 
 julia> st = Optimisers.setup(Nesterov(), m)  # stored momentum is initialised to zero
@@ -27,18 +27,18 @@ julia> st = Optimisers.adjust(st, 0.123)  # change learning rate, stored momentu
 To change other parameters, `adjust` also accepts keyword arguments matching the field
 names of the optimisation rule's type.
 
-```
+```jldoctest adjust
 julia> fieldnames(Adam)
 (:eta, :beta, :epsilon)
 
 julia> st2 = Optimisers.setup(OptimiserChain(ClipGrad(), Adam()), m)
-(vec = Leaf(OptimiserChain(ClipGrad{Float32}(10.0), Adam{Float32}(0.001, (0.9, 0.999), 1.19209f-7)), [nothing, (Float32[0.0, 0.0], Float32[0.0, 0.0], (0.9, 0.999))]), fun = nothing)
+(vec = Leaf(OptimiserChain(ClipGrad{Float32}(10.0), Adam{Float32}(0.001, (0.9, 0.999), 1.19209f-7)), (nothing, (Float32[0.0, 0.0], Float32[0.0, 0.0], (0.9, 0.999)))), fun = ())
 
 julia> Optimisers.adjust(st2; beta = (0.777, 0.909), delta = 11.1)  # delta acts on ClipGrad
-(vec = Leaf(OptimiserChain(ClipGrad{Float32}(11.1), Adam{Float32}(0.001, (0.777, 0.909), 1.19209f-7)), [nothing, (Float32[0.0, 0.0], Float32[0.0, 0.0], (0.9, 0.999))]), fun = nothing)
+(vec = Leaf(OptimiserChain(ClipGrad{Float32}(11.1), Adam{Float32}(0.001, (0.777, 0.909), 1.19209f-7)), (nothing, (Float32[0.0, 0.0], Float32[0.0, 0.0], (0.9, 0.999)))), fun = ())
 
 julia> Optimisers.adjust(st; beta = "no such field")  # silently ignored!
-(vec = Leaf(Nesterov{Float32}(0.001, 0.9), Float32[-0.016, -0.088]), fun = nothing)
+(vec = Leaf(Nesterov{Float32}(0.123, 0.9), Float32[-0.016, -0.088]), fun = ())
 ```
 """
 adjust(tree, eta::Real) = map(st -> adjust(st, eta), tree)
diff --git a/src/rules.jl b/src/rules.jl
index c024feb2..0b366faa 100644
--- a/src/rules.jl
+++ b/src/rules.jl
@@ -607,7 +607,7 @@ julia> o = OptimiserChain(ClipGrad(1.0), Descent(0.1));
 julia> m = (zeros(3),);
 
 julia> s = Optimisers.setup(o, m)
-(Leaf(OptimiserChain(ClipGrad{Float64}(1.0), Descent{Float64}(0.1)), [nothing, nothing]),)
+(Leaf(OptimiserChain(ClipGrad{Float64}(1.0), Descent{Float64}(0.1)), (nothing, nothing)),)
 
 julia> Optimisers.update(s, m, ([0.3, 1, 7],))[2]  # clips before discounting
 ([-0.03, -0.1, -0.1],)
@@ -618,15 +618,15 @@ struct OptimiserChain{O<:Tuple} <: AbstractRule
 end
 OptimiserChain(opts...) = OptimiserChain(opts)
 
-init(o::OptimiserChain, x::AbstractArray) = [init(opt, x) for opt in o.opts]
+@functor OptimiserChain
+
+init(o::OptimiserChain, x::AbstractArray) = map(opt -> init(opt, x), o.opts)
 
 function apply!(o::OptimiserChain, states, x, dx, dxs...)
-  new_states = similar(states)
-  for (i, (opt, state)) in enumerate(zip(o.opts, states))
-    new_states[i], dx = apply!(opt, state, x, dx, dxs...)
+  foldl(tuple.(o.opts, states); init = ((), dx)) do (states′, dx′), (opt, state)
+    state′, dx′ = apply!(opt, state, x, dx′, dxs...)
+    return (states′..., state′), dx′
   end
-
-  return new_states, dx
 end
 
 function Base.show(io::IO, c::OptimiserChain)