diff --git a/src/Optimisers.jl b/src/Optimisers.jl
index cb3afa97..9c3b0be0 100644
--- a/src/Optimisers.jl
+++ b/src/Optimisers.jl
@@ -2,6 +2,8 @@ module Optimisers
 
 using Functors: functor, fmap, isleaf, @functor, fmapstructure, children, AbstractWalk
 using LinearAlgebra
+using ChainRulesCore: canonicalize, backing, Tangent, AbstractZero, ZeroTangent
+using ChainRulesCore: ChainRulesCore, NoTangent, ProjectTo, unthunk
 
 include("interface.jl")
 export AbstractRule
@@ -16,6 +18,8 @@ export Descent, Adam, Momentum, Nesterov, Rprop, RMSProp,
        AdaGrad, AdaMax, AdaDelta, AMSGrad, NAdam, AdamW, RAdam, OAdam, AdaBelief,
        WeightDecay, ClipGrad, ClipNorm, OptimiserChain, Lion
 
+include("deprecations.jl")
+
 ###
 ### one-array functions
 ###
diff --git a/src/deprecations.jl b/src/deprecations.jl
new file mode 100644
index 00000000..94df7bef
--- /dev/null
+++ b/src/deprecations.jl
@@ -0,0 +1,12 @@
+# To be removed in Optimisers v0.3
+
+@deprecate iswriteable maywrite false  # remove when releasing Optimisers@0.3
+
+@deprecate ADAM Adam
+@deprecate NADAM NAdam
+@deprecate ADAMW AdamW
+@deprecate RADAM RAdam
+@deprecate OADAM OAdam
+@deprecate ADAGrad AdaGrad
+@deprecate ADADelta AdaDelta
+
diff --git a/src/destructure.jl b/src/destructure.jl
index 3b21d918..a0c6d2f6 100644
--- a/src/destructure.jl
+++ b/src/destructure.jl
@@ -1,5 +1,4 @@
 
-using ChainRulesCore: ChainRulesCore, NoTangent, ProjectTo, unthunk
 const NoT = NoTangent()
 
 """
diff --git a/src/interface.jl b/src/interface.jl
index 04ead1f3..936d8c3d 100644
--- a/src/interface.jl
+++ b/src/interface.jl
@@ -1,7 +1,7 @@
 
-using ChainRulesCore: canonicalize, backing, Tangent, AbstractZero, ZeroTangent
 base(dx::Tangent) = backing(canonicalize(dx))
 base(dx) = dx
+
 const Zero = Union{Nothing, AbstractZero}  # Union{Zygote, Diffractor}
 
 abstract type AbstractRule end
@@ -15,6 +15,7 @@ mutable struct Leaf{R,S}  # mutable so that its identity encodes parameter shari
   state::S
   frozen::Bool  # ... and to allow freeze! to act on this.
 end
+
 Leaf(rule, state; frozen::Bool = false) = Leaf(rule, state, frozen)
 
 @functor Leaf
@@ -23,9 +24,9 @@ Base.:(==)(a::Leaf, b::Leaf) = children(a) == children(b)
 
 function setup(rule::AbstractRule, model)
   cache = IdDict()
-  tree = _setup(rule, model; cache)
+  state = _setup(rule, model; cache)
   isempty(cache) && @warn "setup found no trainable parameters in this model"
-  tree
+  state
 end
 
 # _setup is almost fmapstructure, but needs a _trainable_walk, and a cache which ignores numbers etc.
@@ -56,38 +57,38 @@ end
 ### update
 ###
 
-function update(tree, model, grad, higher...)
-  t′ = fmap(copy, tree; exclude = maywrite)  # walks inside Leaf
+function update(state, model, grad, higher...)
+  t′ = fmap(copy, state; exclude = maywrite)  # walks inside Leaf
   x′ = fmap(copy, model; exclude = maywrite)
   update!(t′, x′, grad, higher...)
 end
 
-function update!(tree, model, grad, higher...)
+function update!(state, model, grad, higher...)
   # First walk is to accumulate the gradient. This recursion visits every copy of
   # shared leaves, but stops when branches are absent from the gradient:
   grads = IdDict{Leaf, Any}()
-  _grads!(grads, tree, model, grad, higher...)
-  # Second walk is to update the model. The params cache indexed by (tree,x),
+  _grads!(grads, state, model, grad, higher...)
+  # Second walk is to update the model. The params cache indexed by (state,x),
   # so that identified Leafs can tie isbits parameters, but setup won't do that for you:
-  newmodel = _update!(tree, model; grads, params = IdDict())
-  tree, newmodel  # note that tree is guaranteed to be updated. Also that it's not necc a tree.
+  newmodel = _update!(state, model; grads, params = IdDict())
+  state, newmodel  # Note that state is guaranteed to be updated. Also that it's not necc a tree.
 end
 
-function _update!(tree, x; grads, params)
-  haskey(params, (tree,x)) && return params[(tree,x)]
-  isbits(tree) && return x  # means () is not cached, and also (((),),)
+function _update!(state, x; grads, params)
+  haskey(params, (state, x)) && return params[(state, x)]
+  isbits(state) && return x  # means () is not cached, and also (((),),)
   x′, re = functor(x)
-  x′′ = re(valuemap((tᵢ, xᵢ) -> _update!(tᵢ, xᵢ; grads, params), tree, x′))
+  x′′ = re(valuemap((tᵢ, xᵢ) -> _update!(tᵢ, xᵢ; grads, params), state, x′))
   if ismutable(x′′)
-    params[(tree,x)] = x′′
+    params[(state, x)] = x′′
   else  # no ties to preserve between immutable structs, right?
     x′′
   end
 end
 function _update!(ℓ::Leaf, x; grads, params)
-  haskey(params, (ℓ,x)) && return params[(ℓ,x)]
+  haskey(params, (ℓ, x)) && return params[(ℓ, x)]
   ℓ.frozen && return x
-  params[(ℓ,x)] = if haskey(grads, ℓ)
+  params[(ℓ, x)] = if haskey(grads, ℓ)
     ℓ.state, x̄′ = apply!(ℓ.rule, ℓ.state, x, grads[ℓ]...)
     subtract!(x, x̄′)
   else
@@ -98,18 +99,21 @@ end
 subtract!(x, x̄) = maywrite(x) ? (x .= x .- x̄) : eltype(x).(x .- x̄)
 
 _grads!(dict::IdDict, ℓ::Leaf, x, ::Zero...) = nothing
+
 function _grads!(dict::IdDict, ℓ::Leaf, x, x̄s...)
   x̄s₀ = get(dict, ℓ, map(_ -> ZeroTangent(), x̄s))
   dict[ℓ] = map(+, x̄s, x̄s₀)  # adding Zero should be free. Lazy accumulation broadcasted(+, x̄, x̄₀) also possible.
   nothing
 end
+
 _grads!(dict::IdDict, t, x, ::Zero...) = nothing
-function _grads!(dict::IdDict, tree, x, x̄s...)
+
+function _grads!(dict::IdDict, state, x, x̄s...)
   # The only reason _grads! takes model is that functor(typeof(x), base(x̄)) may differ from 
-  # functor(typeof(tree), base(x̄)), for things like Transpose
+  # functor(typeof(state), base(x̄)), for things like Transpose
   x̄s′ = map(x̄ -> functor(typeof(x), base(x̄))[1], x̄s)
   x′, _ = functor(typeof(x), x)
-  valueforeach((tᵢ, xᵢ, x̄sᵢ...) -> _grads!(dict, tᵢ, xᵢ, x̄sᵢ...), tree, x′, x̄s′...)
+  valueforeach((tᵢ, xᵢ, x̄sᵢ...) -> _grads!(dict, tᵢ, xᵢ, x̄sᵢ...), state, x′, x̄s′...)
 end
 
 # default all rules to first order calls
@@ -142,8 +146,6 @@ For now, simply `x isa DenseArray` allowing `Array`, `CuArray`, etc.
 maywrite(::DenseArray) = true  # see https://github.com/FluxML/Optimisers.jl/issues/99 for discussion
 maywrite(_) = false
 
-@deprecate iswriteable maywrite false  # remove when releasing Optimisers@0.3
-
 """
     trainable(x::Layer) -> NamedTuple
 
@@ -175,6 +177,7 @@ end
 
 valuemap(f, x...) = map(f, x...)
 valuemap(f, x::Dict, ys...) = Dict(k => f(v, (get(y, k, nothing) for y in ys)...) for (k,v) in x)
+
 valueforeach(f, x...) = foreach(f, x...)
 valueforeach(f, x::Dict, ys...) = foreach(pairs(x)) do (k, v)
   f(v, (get(y, k, nothing) for y in ys)...)
@@ -216,8 +219,11 @@ macro lazy(ex)
 end
 
 function lazy end
+
 Broadcast.broadcasted(::typeof(lazy), x) = Lazy(x)
+
 struct Lazy{T}; bc::T; end
+
 Broadcast.materialize(x::Lazy) = Broadcast.instantiate(x.bc)
 
 onevalue(λ::T, x::AbstractArray{T}) where T = map(_ -> λ, x)
diff --git a/src/rules.jl b/src/rules.jl
index 57f2d752..898f563c 100644
--- a/src/rules.jl
+++ b/src/rules.jl
@@ -1,11 +1,3 @@
-@deprecate ADAM Adam
-@deprecate NADAM NAdam
-@deprecate ADAMW AdamW
-@deprecate RADAM RAdam
-@deprecate OADAM OAdam
-@deprecate ADAGrad AdaGrad
-@deprecate ADADelta AdaDelta
-
 """
     Descent(η = 1f-1)