diff --git a/Project.toml b/Project.toml
index 5ccbbf752..2ef9edc8b 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.29.2"
+version = "0.29.3"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
diff --git a/src/essential/ad.jl b/src/essential/ad.jl
index 4f07c1eab..5dda65d0f 100644
--- a/src/essential/ad.jl
+++ b/src/essential/ad.jl
@@ -118,7 +118,7 @@ end
 for cache in (:true, :false)
     @eval begin
         function LogDensityProblemsAD.ADgradient(::ReverseDiffAD{$cache}, ℓ::Turing.LogDensityFunction)
-            return LogDensityProblemsAD.ADgradient(Val(:ReverseDiff), ℓ; compile=Val($cache))
+            return LogDensityProblemsAD.ADgradient(Val(:ReverseDiff), ℓ; compile=Val($cache), x=DynamicPPL.getparams(ℓ))
         end
     end
 end
diff --git a/test/essential/ad.jl b/test/essential/ad.jl
index c00f76f12..359101257 100644
--- a/test/essential/ad.jl
+++ b/test/essential/ad.jl
@@ -198,4 +198,18 @@
             end
         end
     end
+
+    @testset "ReverseDiff compiled without linking" begin
+        f = DynamicPPL.LogDensityFunction(gdemo_default)
+        θ = DynamicPPL.getparams(f)
+
+        f_rd = LogDensityProblemsAD.ADgradient(Turing.Essential.ReverseDiffAD{false}(), f)
+        f_rd_compiled = LogDensityProblemsAD.ADgradient(Turing.Essential.ReverseDiffAD{true}(), f)
+
+        ℓ, ℓ_grad = LogDensityProblems.logdensity_and_gradient(f_rd, θ)
+        ℓ_compiled, ℓ_grad_compiled = LogDensityProblems.logdensity_and_gradient(f_rd_compiled, θ)
+
+        @test ℓ == ℓ_compiled
+        @test ℓ_grad == ℓ_grad_compiled
+    end
 end