diff --git a/lib/LuxLib/src/impl/activation.jl b/lib/LuxLib/src/impl/activation.jl
index 0b015e3b13..2dd7df43f8 100644
--- a/lib/LuxLib/src/impl/activation.jl
+++ b/lib/LuxLib/src/impl/activation.jl
@@ -59,7 +59,7 @@ function activation(::AbstractInternalArrayOpMode, σ::F, x::AbstractArray) wher
 end
 @stable default_mode="disable" function activation(
         opmode::LoopedArrayOp, σ::F, x::AbstractArray{T}) where {F, T}
-    RT = Core.Compiler._return_type(σ, Tuple{T})
+    RT = Core.Compiler.return_type(σ, Tuple{T})
     y = similar(x, ifelse(isconcretetype(RT), RT, T))
     activation!(y, opmode, σ, x)
     return y
diff --git a/lib/LuxLib/src/impl/batched_mul.jl b/lib/LuxLib/src/impl/batched_mul.jl
index 37e62de674..0464ca240d 100644
--- a/lib/LuxLib/src/impl/batched_mul.jl
+++ b/lib/LuxLib/src/impl/batched_mul.jl
@@ -17,7 +17,7 @@ end
 
 function batched_matmul(opmode::GPUBroadcastOp{<:AbstractGPUDevice},
         x::AbstractArray{xT, 3}, y::AbstractArray{yT, 3}) where {xT, yT}
-    if isconcretetype(Core.Compiler._return_type(
+    if isconcretetype(Core.Compiler.return_type(
         NNlib.batched_mul, Tuple{typeof(x), typeof(y)}))
         return NNlib.batched_mul(x, y)  # GPU versions are well optimized
     end
diff --git a/lib/LuxLib/src/traits.jl b/lib/LuxLib/src/traits.jl
index 0a768de86f..6df0fc8f7a 100644
--- a/lib/LuxLib/src/traits.jl
+++ b/lib/LuxLib/src/traits.jl
@@ -61,12 +61,12 @@ end
 activation_intermediate_not_needed(::typeof(identity), ::Type) = True()
 
 function activation_intermediate_not_needed(::F, ::Type{T}) where {F, T}
-    return static(isconcretetype(Core.Compiler._return_type(
+    return static(isconcretetype(Core.Compiler.return_type(
         only_derivative, Tuple{T, F, NotaNumber})))
 end
 
 function activation_has_rrule(::F, ::Type{T}) where {F, T}
-    return static(isconcretetype(Core.Compiler._return_type(
+    return static(isconcretetype(Core.Compiler.return_type(
         only_derivative, Tuple{T, F, T})))
 end
 
diff --git a/lib/LuxLib/src/utils.jl b/lib/LuxLib/src/utils.jl
index 1ef926b93a..717df4e78c 100644
--- a/lib/LuxLib/src/utils.jl
+++ b/lib/LuxLib/src/utils.jl
@@ -54,7 +54,7 @@ safe_vec(::Nothing) = nothing
 
 ## This part is taken from NNlib.jl
 # This has no methods, used for testing whether `derivatives_given_output(Ω, f, x)`
-# is independent of `x`, as `_return_type` says `Union{}` when calling is an error.
+# is independent of `x`, as `return_type` says `Union{}` when calling is an error.
 struct NotaNumber <: Real end
 
 # This just saves typing `only.(only.(` many times:
@@ -118,7 +118,7 @@ CRC.@non_differentiable default_epsilon(::Any...)
 function concrete_bias_act_output_eltype(act::F, ::AbstractArray{Tw}, ::AbstractArray{Tx},
         b::Optional{<:AbstractVector}) where {F, Tw, Tx}
     Ty = promote_type(Tw, Tx, safe_eltype(b))
-    Tact = Core.Compiler._return_type(act, Tuple{Ty})
+    Tact = Core.Compiler.return_type(act, Tuple{Ty})
     return ifelse(isconcretetype(Tact), Tact, Ty)
 end
 
diff --git a/src/helpers/losses.jl b/src/helpers/losses.jl
index 8cf17a9971..9a8f575b6b 100644
--- a/src/helpers/losses.jl
+++ b/src/helpers/losses.jl
@@ -42,7 +42,7 @@ end
 fused_agg(::typeof(sum), op::OP, x::Number, y::Number) where {OP} = op(x, y)
 function fused_agg(::typeof(sum), op::OP, x::AbstractArray, y::AbstractArray) where {OP}
     if fast_scalar_indexing(x) && fast_scalar_indexing(y)
-        res = Core.Compiler._return_type(op, Tuple{eltype(x), eltype(y)})(0)
+        res = Core.Compiler.return_type(op, Tuple{eltype(x), eltype(y)})(0)
         @simd ivdep for i in eachindex(x, y)
             @inbounds res += op(x[i], y[i])
         end
@@ -73,7 +73,7 @@ function CRC.rrule(cfg::CRC.RuleConfig{>:CRC.HasReverseMode},
             Nothing, eltype(x), 1}.(x, (Partials{1, eltype(x)}((one(eltype(x)),)),))
         x_partials = similar(x)
         T = eltype(x)
-        res = Core.Compiler._return_type(op, Tuple{T, eltype(y)})(0)
+        res = Core.Compiler.return_type(op, Tuple{T, eltype(y)})(0)
         @inbounds @simd for i in eachindex(x_partials, x, y)
             x_dual = Dual{Nothing, T, 1}(x[i], Partials{1, T}((one(T),)))
             tmp = op(x_dual, y[i])
diff --git a/src/utils.jl b/src/utils.jl
index bd73087aeb..2a6930a2a8 100644
--- a/src/utils.jl
+++ b/src/utils.jl
@@ -42,7 +42,7 @@ unbatched_structure(x) = fmapstructure(size_unbatched, x)
 can_named_tuple(::NamedTuple) = true
 can_named_tuple(::T) where {T} = can_named_tuple(T)
 function can_named_tuple(::Type{T}) where {T}
-    return Core.Compiler._return_type(named_tuple, Tuple{T}) !== Union{}
+    return Core.Compiler.return_type(named_tuple, Tuple{T}) !== Union{}
 end
 
 @non_differentiable can_named_tuple(::Any)
@@ -50,7 +50,7 @@ end
 # Convert to a NamedTuple
 named_tuple(nt::NamedTuple) = nt
 function named_tuple(x::T) where {T}
-    NT = Core.Compiler._return_type(NamedTuple, Tuple{T})
+    NT = Core.Compiler.return_type(NamedTuple, Tuple{T})
     if NT === Union{} || NT === NamedTuple
         error("`NamedTuple` is not defined for type `$(T)`. Please define \
                `Lux.Utils.named_tuple(::$(T))` method (or preferably \