diff --git a/Project.toml b/Project.toml
index d6b8020..adcecd9 100644
--- a/Project.toml
+++ b/Project.toml
@@ -3,7 +3,7 @@ uuid = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 authors = [
     "Vaibhav Dixit <vaibhavyashdixit@gmail.com>, Guillaume Dalle and contributors",
 ]
-version = "1.6.1"
+version = "1.6.2"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
diff --git a/src/dense.jl b/src/dense.jl
index 6757476..8e6d960 100644
--- a/src/dense.jl
+++ b/src/dense.jl
@@ -39,7 +39,7 @@ struct AutoDiffractor <: AbstractADType end
 mode(::AutoDiffractor) = ForwardOrReverseMode()
 
 """
-    AutoEnzyme{M,constant_function}
+    AutoEnzyme{M}
 
 Struct used to select the [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl) backend for automatic differentiation.
 
@@ -47,11 +47,7 @@ Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
 
 # Constructors
 
-    AutoEnzyme(; mode=nothing, constant_function::Bool=false)
-
-The `constant_function` keyword argument (and type parameter) determines whether the function object itself should be considered constant or not during differentiation with Enzyme.jl.
-For simple functions, `constant_function` should usually be set to `true`, which leads to increased performance.
-However, in the case of closures or callable structs which contain differentiated data, `constant_function` should be set to `false` to ensure correctness (more details below).
+    AutoEnzyme(; mode=nothing)
 
 # Fields
 
@@ -59,53 +55,13 @@ However, in the case of closures or callable structs which contain differentiate
 
       + an object subtyping `EnzymeCore.Mode` (like `EnzymeCore.Forward` or `EnzymeCore.Reverse`) if a specific mode is required
       + `nothing` to choose the best mode automatically
-
-# Notes
-
-We now give several examples of functions.
-For each one, we explain how `constant_function` should be set in order to compute the correct derivative with respect to the input `x`.
-
-```julia
-function f1(x)
-    return x[1]
-end
-```
-
-The function `f1` is not a closure, it does not contain any data.
-Thus `f1` can be differentiated with `AutoEnzyme(constant_function=true)` (although here setting `constant_function=false` would change neither correctness nor performance).
-
-```julia
-parameter = [0.0]
-function f2(x)
-    return parameter[1] + x[1]
-end
-```
-
-The function `f2` is a closure over `parameter`, but `parameter` is never modified based on the input `x`.
-Thus, `f2` can be differentiated with `AutoEnzyme(constant_function=true)` (setting `constant_function=false` would not change correctness but would hinder performance).
-
-```julia
-cache = [0.0]
-function f3(x)
-    cache[1] = x[1]
-    return cache[1] + x[1]
-end
-```
-
-The function `f3` is a closure over `cache`, and `cache` is modified based on the input `x`.
-That means `cache` cannot be treated as constant, since derivative values must be propagated through it.
-Thus `f3` must be differentiated with `AutoEnzyme(constant_function=false)` (setting `constant_function=true` would make the result incorrect).
 """
-struct AutoEnzyme{M, constant_function} <: AbstractADType
+struct AutoEnzyme{M} <: AbstractADType
     mode::M
 end
 
-function AutoEnzyme(mode::M; constant_function::Bool = false) where {M}
-    return AutoEnzyme{M, constant_function}(mode)
-end
-
-function AutoEnzyme(; mode::M = nothing, constant_function::Bool = false) where {M}
-    return AutoEnzyme{M, constant_function}(mode)
+function AutoEnzyme(; mode::M = nothing) where {M}
+    return AutoEnzyme{M}(mode)
 end
 
 mode(::AutoEnzyme) = ForwardOrReverseMode()  # specialized in the extension
diff --git a/test/dense.jl b/test/dense.jl
index 739cf59..7554565 100644
--- a/test/dense.jl
+++ b/test/dense.jl
@@ -28,25 +28,25 @@ end
 @testset "AutoEnzyme" begin
     ad = AutoEnzyme()
     @test ad isa AbstractADType
-    @test ad isa AutoEnzyme{Nothing, false}
+    @test ad isa AutoEnzyme{Nothing}
     @test mode(ad) isa ForwardOrReverseMode
     @test ad.mode === nothing
 
-    ad = AutoEnzyme(EnzymeCore.Forward; constant_function = true)
+    ad = AutoEnzyme(EnzymeCore.Forward)
     @test ad isa AbstractADType
-    @test ad isa AutoEnzyme{typeof(EnzymeCore.Forward), true}
+    @test ad isa AutoEnzyme{typeof(EnzymeCore.Forward)}
     @test mode(ad) isa ForwardMode
     @test ad.mode == EnzymeCore.Forward
 
     ad = AutoEnzyme(; mode = EnzymeCore.Forward)
     @test ad isa AbstractADType
-    @test ad isa AutoEnzyme{typeof(EnzymeCore.Forward), false}
+    @test ad isa AutoEnzyme{typeof(EnzymeCore.Forward)}
     @test mode(ad) isa ForwardMode
     @test ad.mode == EnzymeCore.Forward
 
-    ad = AutoEnzyme(; mode = EnzymeCore.Reverse, constant_function = true)
+    ad = AutoEnzyme(; mode = EnzymeCore.Reverse)
     @test ad isa AbstractADType
-    @test ad isa AutoEnzyme{typeof(EnzymeCore.Reverse), true}
+    @test ad isa AutoEnzyme{typeof(EnzymeCore.Reverse)}
     @test mode(ad) isa ReverseMode
     @test ad.mode == EnzymeCore.Reverse
 end