diff --git a/src/mcmc/gibbs.jl b/src/mcmc/gibbs.jl
index a5c6f99f7..0f4cd2ecf 100644
--- a/src/mcmc/gibbs.jl
+++ b/src/mcmc/gibbs.jl
@@ -429,49 +429,49 @@ recursively on the remaining samplers, until no samplers remain. Return the glob
 and a tuple of initial states for all component samplers.
 """
 function gibbs_initialstep_recursive(
-    rng, model, varnames, samplers, vi, states=(); initial_params=nothing, kwargs...
+    rng, model, varname_vecs, samplers, vi, states=(); initial_params=nothing, kwargs...
 )
     # End recursion
-    if isempty(varnames) && isempty(samplers)
+    if isempty(varname_vecs) && isempty(samplers)
         return vi, states
     end
 
-    varnames_local = first(varnames)
-    sampler_local = first(samplers)
+    varnames, varname_vecs_tail... = varname_vecs
+    sampler, samplers_tail... = samplers
 
     # Get the initial values for this component sampler.
     initial_params_local = if initial_params === nothing
         nothing
     else
-        DynamicPPL.subset(vi, varnames_local)[:]
+        DynamicPPL.subset(vi, varnames)[:]
     end
 
     # Construct the conditioned model.
-    model_local, context_local = make_conditional(model, varnames_local, vi)
+    conditioned_model, context = make_conditional(model, varnames, vi)
 
-    # Take initial step.
-    _, new_state_local = AbstractMCMC.step(
+    # Take initial step with the current sampler.
+    _, new_state = AbstractMCMC.step(
         rng,
-        model_local,
-        sampler_local;
+        conditioned_model,
+        sampler;
         # FIXME: This will cause issues if the sampler expects initial params in unconstrained space.
         # This is not the case for any samplers in Turing.jl, but will be for external samplers, etc.
         initial_params=initial_params_local,
         kwargs...,
     )
-    new_vi_local = varinfo(new_state_local)
+    new_vi_local = varinfo(new_state)
     # Merge in any new variables that were introduced during the step, but that
     # were not in the domain of the current sampler.
-    vi = merge(vi, get_global_varinfo(context_local))
+    vi = merge(vi, get_global_varinfo(context))
     # Merge the new values for all the variables sampled by the current sampler.
     vi = merge(vi, new_vi_local)
 
-    states = (states..., new_state_local)
+    states = (states..., new_state)
     return gibbs_initialstep_recursive(
         rng,
         model,
-        varnames[2:end],
-        samplers[2:end],
+        varname_vecs_tail,
+        samplers_tail,
         vi,
         states;
         initial_params=initial_params,
@@ -624,7 +624,7 @@ function on the tail, until there are no more samplers left.
 function gibbs_step_recursive(
     rng::Random.AbstractRNG,
     model::DynamicPPL.Model,
-    varnames,
+    varname_vecs,
     samplers,
     states,
     global_vi,
@@ -632,18 +632,18 @@ function gibbs_step_recursive(
     kwargs...,
 )
     # End recursion.
-    if isempty(varnames) && isempty(samplers) && isempty(states)
+    if isempty(varname_vecs) && isempty(samplers) && isempty(states)
         return global_vi, new_states
     end
 
-    varnames_local = first(varnames)
-    sampler_local = first(samplers)
-    state_local = first(states)
+    varnames, varname_vecs_tail... = varname_vecs
+    sampler, samplers_tail... = samplers
+    state, states_tail... = states
 
     # Construct the conditional model and the varinfo that this sampler should use.
-    model_local, context_local = make_conditional(model, varnames_local, global_vi)
-    varinfo_local = subset(global_vi, varnames_local)
-    varinfo_local = match_linking!!(varinfo_local, state_local, model)
+    conditioned_model, context = make_conditional(model, varnames, global_vi)
+    vi = subset(global_vi, varnames)
+    vi = match_linking!!(vi, state, model)
 
     # TODO(mhauru) The below may be overkill. If the varnames for this sampler are not
     # sampled by other samplers, we don't need to `setparams`, but could rather simply
@@ -654,27 +654,27 @@ function gibbs_step_recursive(
     # going to be a significant expense anyway.
     # Set the state of the current sampler, accounting for any changes made by other
     # samplers.
-    state_local = setparams_varinfo!!(
-        model_local, sampler_local, state_local, varinfo_local
+    state = setparams_varinfo!!(
+        conditioned_model, sampler, state, vi
     )
 
     # Take a step with the local sampler.
-    new_state_local = last(
-        AbstractMCMC.step(rng, model_local, sampler_local, state_local; kwargs...)
+    new_state = last(
+        AbstractMCMC.step(rng, conditioned_model, sampler, state; kwargs...)
     )
 
-    new_vi_local = varinfo(new_state_local)
+    new_vi_local = varinfo(new_state)
     # Merge the latest values for all the variables in the current sampler.
-    new_global_vi = merge(get_global_varinfo(context_local), new_vi_local)
+    new_global_vi = merge(get_global_varinfo(context), new_vi_local)
     new_global_vi = setlogp!!(new_global_vi, getlogp(new_vi_local))
 
-    new_states = (new_states..., new_state_local)
+    new_states = (new_states..., new_state)
     return gibbs_step_recursive(
         rng,
         model,
-        varnames[2:end],
-        samplers[2:end],
-        states[2:end],
+        varname_vecs_tail,
+        samplers_tail,
+        states_tail,
         new_global_vi,
         new_states;
         kwargs...,