diff --git a/ext/DistributionsChainRulesCoreExt/eachvariate.jl b/ext/DistributionsChainRulesCoreExt/eachvariate.jl
index 359a6703eb..454d203359 100644
--- a/ext/DistributionsChainRulesCoreExt/eachvariate.jl
+++ b/ext/DistributionsChainRulesCoreExt/eachvariate.jl
@@ -1,4 +1,4 @@
-function ChainRulesCore.rrule(::Type{Distributions.EachVariate{V}}, x::AbstractArray{<:Real}) where {V}
+function ChainRulesCore.rrule(::ChainRulesCore.RuleConfig, ::Type{Distributions.EachVariate{V}}, x::AbstractArray{<:Real}) where {V}
     y = Distributions.EachVariate{V}(x)
     size_x = size(x)
     function EachVariate_pullback(Δ)
diff --git a/ext/DistributionsChainRulesCoreExt/multivariate/dirichlet.jl b/ext/DistributionsChainRulesCoreExt/multivariate/dirichlet.jl
index 5aa0d727d5..69b0dbe18c 100644
--- a/ext/DistributionsChainRulesCoreExt/multivariate/dirichlet.jl
+++ b/ext/DistributionsChainRulesCoreExt/multivariate/dirichlet.jl
@@ -1,4 +1,4 @@
-function ChainRulesCore.frule((_, Δalpha)::Tuple{Any,Any}, ::Type{DT}, alpha::AbstractVector{T}; check_args::Bool = true) where {T <: Real, DT <: Union{Dirichlet{T}, Dirichlet}}
+function ChainRulesCore.frule(::ChainRulesCore.RuleConfig, (_, Δalpha)::Tuple{Any,Any}, ::Type{DT}, alpha::AbstractVector{T}; check_args::Bool = true) where {T <: Real, DT <: Union{Dirichlet{T}, Dirichlet}}
     d = DT(alpha; check_args=check_args)
     ∂alpha0 = sum(Δalpha)
     digamma_alpha0 = SpecialFunctions.digamma(d.alpha0)
@@ -9,7 +9,7 @@ function ChainRulesCore.frule((_, Δalpha)::Tuple{Any,Any}, ::Type{DT}, alpha::A
     return d, Δd
 end
 
-function ChainRulesCore.rrule(::Type{DT}, alpha::AbstractVector{T}; check_args::Bool = true) where {T <: Real, DT <: Union{Dirichlet{T}, Dirichlet}}
+function ChainRulesCore.rrule(::ChainRulesCore.RuleConfig, ::Type{DT}, alpha::AbstractVector{T}; check_args::Bool = true) where {T <: Real, DT <: Union{Dirichlet{T}, Dirichlet}}
     d = DT(alpha; check_args=check_args)
     digamma_alpha0 = SpecialFunctions.digamma(d.alpha0)
     function Dirichlet_pullback(_Δd)
@@ -20,7 +20,7 @@ function ChainRulesCore.rrule(::Type{DT}, alpha::AbstractVector{T}; check_args::
     return d, Dirichlet_pullback
 end
 
-function ChainRulesCore.frule((_, Δd, Δx)::Tuple{Any,Any,Any}, ::typeof(Distributions._logpdf), d::Dirichlet, x::AbstractVector{<:Real})
+function ChainRulesCore.frule(::ChainRulesCore.RuleConfig, (_, Δd, Δx)::Tuple{Any,Any,Any}, ::typeof(Distributions._logpdf), d::Dirichlet, x::AbstractVector{<:Real})
     Ω = Distributions._logpdf(d, x)
     ∂alpha = sum(Broadcast.instantiate(Broadcast.broadcasted(Δd.alpha, Δx, d.alpha, x) do Δalphai, Δxi, alphai, xi
         StatsFuns.xlogy(Δalphai, xi) + (alphai - 1) * Δxi / xi
@@ -33,7 +33,7 @@ function ChainRulesCore.frule((_, Δd, Δx)::Tuple{Any,Any,Any}, ::typeof(Distri
     return Ω, ΔΩ
 end
 
-function ChainRulesCore.rrule(::typeof(Distributions._logpdf), d::T, x::AbstractVector{<:Real}) where {T<:Dirichlet}
+function ChainRulesCore.rrule(::ChainRulesCore.RuleConfig, ::typeof(Distributions._logpdf), d::T, x::AbstractVector{<:Real}) where {T<:Dirichlet}
     Ω = Distributions._logpdf(d, x)
     isfinite_Ω = isfinite(Ω)
     alpha = d.alpha
diff --git a/ext/DistributionsChainRulesCoreExt/univariate/continuous/uniform.jl b/ext/DistributionsChainRulesCoreExt/univariate/continuous/uniform.jl
index 0461329577..9acaa06910 100644
--- a/ext/DistributionsChainRulesCoreExt/univariate/continuous/uniform.jl
+++ b/ext/DistributionsChainRulesCoreExt/univariate/continuous/uniform.jl
@@ -1,4 +1,4 @@
-function ChainRulesCore.frule((_, Δd, _), ::typeof(logpdf), d::Uniform, x::Real)
+function ChainRulesCore.frule(::ChainRulesCore.RuleConfig, (_, Δd, _), ::typeof(logpdf), d::Uniform, x::Real)
     # Compute log probability
     a, b = params(d)
     insupport = a <= x <= b
@@ -12,7 +12,7 @@ function ChainRulesCore.frule((_, Δd, _), ::typeof(logpdf), d::Uniform, x::Real
     return Ω, ΔΩ
 end
 
-function ChainRulesCore.rrule(::typeof(logpdf), d::Uniform, x::Real)
+function ChainRulesCore.rrule(::ChainRulesCore.RuleConfig, ::typeof(logpdf), d::Uniform, x::Real)
     # Compute log probability
     a, b = params(d)
     insupport = a <= x <= b
diff --git a/ext/DistributionsChainRulesCoreExt/univariate/discrete/negativebinomial.jl b/ext/DistributionsChainRulesCoreExt/univariate/discrete/negativebinomial.jl
index 06ee0294f7..0a29dbe7cb 100644
--- a/ext/DistributionsChainRulesCoreExt/univariate/discrete/negativebinomial.jl
+++ b/ext/DistributionsChainRulesCoreExt/univariate/discrete/negativebinomial.jl
@@ -11,7 +11,7 @@ function (f::LogPDFNegativeBinomialPullback{D})(Δ) where {D}
     return ChainRulesCore.NoTangent(), Δd, ChainRulesCore.NoTangent()
 end
 
-function ChainRulesCore.rrule(::typeof(logpdf), d::NegativeBinomial, k::Real)
+function ChainRulesCore.rrule(::ChainRulesCore.RuleConfig, ::typeof(logpdf), d::NegativeBinomial, k::Real)
     # Compute log probability (as in the definition of `logpdf(d, k)` above)
     r, p = params(d)
     z = StatsFuns.xlogy(r, p) + StatsFuns.xlog1py(k, -p)
diff --git a/ext/DistributionsChainRulesCoreExt/univariate/discrete/poissonbinomial.jl b/ext/DistributionsChainRulesCoreExt/univariate/discrete/poissonbinomial.jl
index aa27a9fd94..65d77942d3 100644
--- a/ext/DistributionsChainRulesCoreExt/univariate/discrete/poissonbinomial.jl
+++ b/ext/DistributionsChainRulesCoreExt/univariate/discrete/poissonbinomial.jl
@@ -2,13 +2,13 @@ for f in (:poissonbinomial_pdf, :poissonbinomial_pdf_fft)
     pullback = Symbol(f, :_pullback)
     @eval begin
         function ChainRulesCore.frule(
-            (_, Δp)::Tuple{<:Any,<:AbstractVector{<:Real}}, ::typeof(Distributions.$f), p::AbstractVector{<:Real}
+            ::ChainRulesCore.RuleConfig, (_, Δp)::Tuple{<:Any,<:AbstractVector{<:Real}}, ::typeof(Distributions.$f), p::AbstractVector{<:Real}
         )
             y = Distributions.$f(p)
             A = Distributions.poissonbinomial_pdf_partialderivatives(p)
             return y, A' * Δp
         end
-        function ChainRulesCore.rrule(::typeof(Distributions.$f), p::AbstractVector{<:Real})
+        function ChainRulesCore.rrule(::ChainRulesCore.RuleConfig, ::typeof(Distributions.$f), p::AbstractVector{<:Real})
             y = Distributions.$f(p)
             A = Distributions.poissonbinomial_pdf_partialderivatives(p)
             function $pullback(Δy)