diff --git a/Project.toml b/Project.toml
index 2bcc8bee5..cea6b3655 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.35.3"
+version = "0.35.4"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
diff --git a/src/mcmc/hmc.jl b/src/mcmc/hmc.jl
index 5887feb5e..5f1caead2 100644
--- a/src/mcmc/hmc.jl
+++ b/src/mcmc/hmc.jl
@@ -463,6 +463,12 @@ end
 
 getstepsize(sampler::Sampler{<:Hamiltonian}, state) = sampler.alg.ϵ
 getstepsize(sampler::Sampler{<:AdaptiveHamiltonian}, state) = AHMC.getϵ(state.adaptor)
+function getstepsize(
+    sampler::Sampler{<:AdaptiveHamiltonian},
+    state::HMCState{TV,TKernel,THam,PhType,AHMC.Adaptation.NoAdaptation},
+) where {TV,TKernel,THam,PhType}
+    return state.kernel.τ.integrator.ϵ
+end
 
 gen_metric(dim::Int, spl::Sampler{<:Hamiltonian}, state) = AHMC.UnitEuclideanMetric(dim)
 function gen_metric(dim::Int, spl::Sampler{<:AdaptiveHamiltonian}, state)
diff --git a/test/mcmc/hmc.jl b/test/mcmc/hmc.jl
index 7d3f3a380..6b07c7378 100644
--- a/test/mcmc/hmc.jl
+++ b/test/mcmc/hmc.jl
@@ -329,6 +329,19 @@ using Turing
         @test pvalue(ApproximateTwoSampleKSTest(vec(results), vec(results_prior))) > 0.001
     end
 
+    @testset "getstepsize: Turing.jl#2400" begin
+        algs = [HMC(0.1, 10), HMCDA(0.8, 0.75), NUTS(0.5), NUTS(0, 0.5)]
+        @testset "$(alg)" for alg in algs
+            # Construct a HMC state by taking a single step
+            spl = Sampler(alg, gdemo_default)
+            hmc_state = DynamicPPL.initialstep(
+                Random.default_rng(), gdemo_default, spl, DynamicPPL.VarInfo(gdemo_default)
+            )[2]
+            # Check that we can obtain the current step size
+            @test Turing.Inference.getstepsize(spl, hmc_state) isa Float64
+        end
+    end
+
     @testset "Check ADType" begin
         alg = HMC(0.1, 10; adtype=adbackend)
         m = DynamicPPL.contextualize(