diff --git a/ext/OptimizationEnzymeExt.jl b/ext/OptimizationEnzymeExt.jl
index 2c758e5bc..b419b0d8d 100644
--- a/ext/OptimizationEnzymeExt.jl
+++ b/ext/OptimizationEnzymeExt.jl
@@ -9,38 +9,34 @@ isdefined(Base, :get_extension) ? (using Enzyme) : (using ..Enzyme)
 function Optimization.instantiate_function(f::OptimizationFunction{true}, x,
     adtype::AutoEnzyme, p,
     num_cons = 0)
-    _f = (θ, y, args...) -> (y .= first(f.f(θ, p, args...)); return nothing)
+    _f = (f, θ, args...) -> first(f(θ, p, args...))
 
     if f.grad === nothing
         function grad(res, θ, args...)
             res .= zero(eltype(res))
-            Enzyme.autodiff(Enzyme.Reverse, _f, Enzyme.Duplicated(θ, res),
-                            Enzyme.DuplicatedNoNeed(zeros(1), ones(1)), args...)
+            Enzyme.autodiff(Enzyme.Reverse, Const(_f), Const(f.f), Enzyme.Duplicated(θ, res), args...)
         end
     else
         grad = (G, θ, args...) -> f.grad(G, θ, p, args...)
     end
 
     if f.hess === nothing
-        function g(θ, bθ, y, by, args...)
-            Enzyme.autodiff_deferred(Enzyme.Reverse, _f, Enzyme.Duplicated(θ, bθ), Enzyme.DuplicatedNoNeed(y, by), args...)
+        function g(θ, bθ, _f, f, args...)
+            Enzyme.autodiff_deferred(Enzyme.Reverse, Const(_f), Const(f), Enzyme.Duplicated(θ, bθ), args...)
             return nothing
         end
         function hess(res, θ, args...)
-            y = Vector{Float64}(undef, 1)
-
             vdθ = Tuple((Array(r) for r in eachrow(I(length(θ)) * 1.0)))
 
             bθ = zeros(length(θ))
-            by = ones(1)
             vdbθ = Tuple(zeros(length(θ)) for i in eachindex(θ))
 
             Enzyme.autodiff(Enzyme.Forward,
                 g,
                 Enzyme.BatchDuplicated(θ, vdθ),
                 Enzyme.BatchDuplicated(bθ, vdbθ),
-                Const(y),
-                Const(by),
+                Const(_f),
+                Const(f.f),
                 args...)
 
             for i in eachindex(θ)
@@ -52,16 +48,17 @@ function Optimization.instantiate_function(f::OptimizationFunction{true}, x,
     end
 
     if f.hv === nothing
+        function f2(x, v, _f, f, args...)::Float64
+            dx = zeros(length(x))
+            Enzyme.autodiff_deferred(Enzyme.Reverse, Const(_f),
+                                     Const(f),
+                                     Enzyme.Duplicated(x, dx),
+                                     args...)
+            Float64(dot(dx, v))
+        end
         hv = function (H, θ, v, args...)
-            function f2(x, v, args...)::Float64
-                dx = zeros(length(x))
-                Enzyme.autodiff_deferred(Enzyme.Reverse, _f,
-                                         Enzyme.Duplicated(x, dx),
-                                         Enzyme.DuplicatedNoNeed(zeros(1), ones(1)),
-                                         args...)
-                Float64(dot(dx, v))
-            end
-            H .= Enzyme.gradient(Enzyme.Forward, x -> f2(x, v, args...), θ)
+            H .= zero(eltype(H))
+            Enzyme.autodiff(Enzyme.Forward, f2, Duplicated(θ, H), v, Const(_f), Const(f.f), args...)
         end
     else
         hv = f.hv
@@ -119,39 +116,35 @@ function Optimization.instantiate_function(f::OptimizationFunction{true},
     cache::Optimization.ReInitCache,
     adtype::AutoEnzyme,
     num_cons = 0)
-    _f = (θ, y, args...) -> (y .= first(f.f(θ, cache.p, args...)); return nothing)
+    _f = (f, θ, args...) -> first(f(θ, cache.p, args...))
 
     if f.grad === nothing
         function grad(res, θ, args...)
             res .= zero(eltype(res))
-            Enzyme.autodiff(Enzyme.Reverse, _f, Enzyme.Duplicated(θ, res),
-                            Enzyme.DuplicatedNoNeed(zeros(1), ones(1)), args...)
+            Enzyme.autodiff(Enzyme.Reverse, Const(_f), Const(f.f), Enzyme.Duplicated(θ, res), args...)
         end
     else
         grad = (G, θ, args...) -> f.grad(G, θ, p, args...)
     end
 
     if f.hess === nothing
-        function g(θ, bθ, y, by, args...)
-            Enzyme.autodiff_deferred(Enzyme.Reverse, _f, Enzyme.Duplicated(θ, bθ),
-                                     Enzyme.DuplicatedNoNeed(y, by), args...)
+        function g(θ, bθ, _f, f, args...)
+            Enzyme.autodiff_deferred(Enzyme.Reverse, Const(_f), Const(f), Enzyme.Duplicated(θ, bθ),
+                                     args...)
             return nothing
         end
         function hess(res, θ, args...)
-            y = Vector{Float64}(undef, 1)
-
             vdθ = Tuple((Array(r) for r in eachrow(I(length(θ)) * 1.0)))
 
             bθ = zeros(length(θ))
-            by = ones(1)
             vdbθ = Tuple(zeros(length(θ)) for i in eachindex(θ))
 
             Enzyme.autodiff(Enzyme.Forward,
                             g,
                             Enzyme.BatchDuplicated(θ, vdθ),
                             Enzyme.BatchDuplicated(bθ, vdbθ),
-                            Const(y),
-                            Const(by),
+                            Const(_f),
+                            Const(f.f),
                             args...)
 
             for i in eachindex(θ)
@@ -163,16 +156,17 @@ function Optimization.instantiate_function(f::OptimizationFunction{true},
     end
 
     if f.hv === nothing
+        function f2(x, v, _f, f, args...)::Float64
+            dx = zeros(length(x))
+            Enzyme.autodiff_deferred(Enzyme.Reverse, Const(_f),
+                                     Const(f),
+                                     Enzyme.Duplicated(x, dx),
+                                     args...)
+            Float64(dot(dx, v))
+        end
         hv = function (H, θ, v, args...)
-            function f2(x, v, args...)::Float64
-                dx = zeros(length(x))
-                Enzyme.autodiff_deferred(Enzyme.Reverse, _f,
-                                         Enzyme.Duplicated(x, dx),
-                                         Enzyme.DuplicatedNoNeed(zeros(1), ones(1)),
-                                         args...)
-                Float64(dot(dx, v))
-            end
-            H .= Enzyme.gradient(Enzyme.Forward, x -> f2(x, v, args...), θ)
+            H .= zero(eltype(H))
+            Enzyme.autodiff(Enzyme.Forward, f2, Duplicated(θ, H), v, Const(_f), Const(f.f), args...)
         end
     else
         hv = f.hv