diff --git a/ext/OptimizationZygoteExt.jl b/ext/OptimizationZygoteExt.jl
index a2a4b6d..7574af7 100644
--- a/ext/OptimizationZygoteExt.jl
+++ b/ext/OptimizationZygoteExt.jl
@@ -30,7 +30,7 @@ function OptimizationBase.instantiate_function(
     adtype, soadtype = OptimizationBase.generate_adtype(adtype)
 
     if g == true && f.grad === nothing
-        prep_grad = prepare_gradient(f.f, adtype, x, Constant(p))
+        prep_grad = prepare_gradient(f.f, adtype, x, Constant(p), strict=Val(false))
         function grad(res, θ)
             gradient!(f.f, res, prep_grad, adtype, θ, Constant(p))
         end
@@ -47,7 +47,7 @@ function OptimizationBase.instantiate_function(
 
     if fg == true && f.fg === nothing
         if g == false
-            prep_grad = prepare_gradient(f.f, adtype, x, Constant(p))
+            prep_grad = prepare_gradient(f.f, adtype, x, Constant(p), strict=Val(false))
         end
         function fg!(res, θ)
             (y, _) = value_and_gradient!(f.f, res, prep_grad, adtype, θ, Constant(p))
@@ -68,7 +68,7 @@ function OptimizationBase.instantiate_function(
     hess_sparsity = f.hess_prototype
     hess_colors = f.hess_colorvec
     if h == true && f.hess === nothing
-        prep_hess = prepare_hessian(f.f, soadtype, x, Constant(p))
+        prep_hess = prepare_hessian(f.f, soadtype, x, Constant(p), strict=Val(false))
         function hess(res, θ)
             hessian!(f.f, res, prep_hess, soadtype, θ, Constant(p))
         end
@@ -143,7 +143,7 @@ function OptimizationBase.instantiate_function(
     cons_jac_prototype = f.cons_jac_prototype
     cons_jac_colorvec = f.cons_jac_colorvec
     if cons !== nothing && cons_j == true && f.cons_j === nothing
-        prep_jac = prepare_jacobian(cons_oop, adtype, x)
+        prep_jac = prepare_jacobian(cons_oop, adtype, x, strict=Val(false))
         function cons_j!(J, θ)
             jacobian!(cons_oop, J, prep_jac, adtype, θ)
             if size(J, 1) == 1
@@ -157,7 +157,7 @@ function OptimizationBase.instantiate_function(
     end
 
     if f.cons_vjp === nothing && cons_vjp == true && cons !== nothing
-        prep_pullback = prepare_pullback(cons_oop, adtype, x, (ones(eltype(x), num_cons),))
+        prep_pullback = prepare_pullback(cons_oop, adtype, x, (ones(eltype(x), num_cons),), strict=Val(false))
         function cons_vjp!(J, θ, v)
             pullback!(cons_oop, (J,), prep_pullback, adtype, θ, (v,))
         end
@@ -169,7 +169,7 @@ function OptimizationBase.instantiate_function(
 
     if cons !== nothing && f.cons_jvp === nothing && cons_jvp == true
         prep_pushforward = prepare_pushforward(
-            cons_oop, adtype, x, (ones(eltype(x), length(x)),))
+            cons_oop, adtype, x, (ones(eltype(x), length(x)),), strict=Val(false))
         function cons_jvp!(J, θ, v)
             pushforward!(cons_oop, (J,), prep_pushforward, adtype, θ, (v,))
         end
@@ -182,7 +182,7 @@ function OptimizationBase.instantiate_function(
     conshess_sparsity = f.cons_hess_prototype
     conshess_colors = f.cons_hess_colorvec
     if cons !== nothing && cons_h == true && f.cons_h === nothing
-        prep_cons_hess = [prepare_hessian(cons_oop, soadtype, x, Constant(i))
+        prep_cons_hess = [prepare_hessian(cons_oop, soadtype, x, Constant(i), strict=Val(false))
                           for i in 1:num_cons]
 
         function cons_h!(H, θ)
@@ -201,7 +201,7 @@ function OptimizationBase.instantiate_function(
     if f.lag_h === nothing && cons !== nothing && lag_h == true
         lag_extras = prepare_hessian(
             lagrangian, soadtype, x, Constant(one(eltype(x))),
-            Constant(ones(eltype(x), num_cons)), Constant(p))
+            Constant(ones(eltype(x), num_cons)), Constant(p), strict=Val(false))
         lag_hess_prototype = zeros(Bool, num_cons, length(x))
 
         function lag_h!(H::AbstractMatrix, θ, σ, λ)
@@ -294,7 +294,7 @@ function OptimizationBase.instantiate_function(
     adtype, soadtype = OptimizationBase.generate_sparse_adtype(adtype)
 
     if g == true && f.grad === nothing
-        extras_grad = prepare_gradient(f.f, adtype.dense_ad, x, Constant(p))
+        extras_grad = prepare_gradient(f.f, adtype.dense_ad, x, Constant(p), strict=Val(false))
         function grad(res, θ)
             gradient!(f.f, res, extras_grad, adtype.dense_ad, θ, Constant(p))
         end
@@ -311,7 +311,7 @@ function OptimizationBase.instantiate_function(
 
     if fg == true && f.fg === nothing
         if g == false
-            extras_grad = prepare_gradient(f.f, adtype.dense_ad, x, Constant(p))
+            extras_grad = prepare_gradient(f.f, adtype.dense_ad, x, Constant(p), strict=Val(false))
         end
         function fg!(res, θ)
             (y, _) = value_and_gradient!(
@@ -334,7 +334,7 @@ function OptimizationBase.instantiate_function(
     hess_sparsity = f.hess_prototype
     hess_colors = f.hess_colorvec
     if h == true && f.hess === nothing
-        prep_hess = prepare_hessian(f.f, soadtype, x, Constant(p))
+        prep_hess = prepare_hessian(f.f, soadtype, x, Constant(p), strict=Val(false))
         function hess(res, θ)
             hessian!(f.f, res, prep_hess, soadtype, θ, Constant(p))
         end
@@ -458,7 +458,7 @@ function OptimizationBase.instantiate_function(
     conshess_sparsity = f.cons_hess_prototype
     conshess_colors = f.cons_hess_colorvec
     if cons !== nothing && f.cons_h === nothing && cons_h == true
-        prep_cons_hess = [prepare_hessian(cons_oop, soadtype, x, Constant(i))
+        prep_cons_hess = [prepare_hessian(cons_oop, soadtype, x, Constant(i), strict=Val(false))
                           for i in 1:num_cons]
         colores = getfield.(prep_cons_hess, :coloring_result)
         conshess_sparsity = getfield.(colores, :A)
@@ -479,7 +479,7 @@ function OptimizationBase.instantiate_function(
     if cons !== nothing && f.lag_h === nothing && lag_h == true
         lag_extras = prepare_hessian(
             lagrangian, soadtype, x, Constant(one(eltype(x))),
-            Constant(ones(eltype(x), num_cons)), Constant(p))
+            Constant(ones(eltype(x), num_cons)), Constant(p), strict=Val(false))
         lag_hess_prototype = lag_extras.coloring_result.A
         lag_hess_colors = lag_extras.coloring_result.color