diff --git a/packages/CLPBN/clpbn.yap b/packages/CLPBN/clpbn.yap
index 91b148b66..53d31b653 100644
--- a/packages/CLPBN/clpbn.yap
+++ b/packages/CLPBN/clpbn.yap
@@ -10,9 +10,9 @@
 		 clpbn_init_graph/1,
 		 clpbn_init_solver/4,
 		 clpbn_run_solver/3,
-		 clpbn_finalize_solver/1,
 		 pfl_init_solver/5,
 		 pfl_run_solver/3,
+		 pfl_end_solver/1,
 		 probability/2,
 		 conditional_probability/3,
 		 use_parfactors/1,
@@ -662,17 +662,9 @@ clpbn_run_solver(gibbs, LVs, LPs, State) :-
 clpbn_run_solver(pcg, LVs, LPs, State) :-
 	run_pcg_solver(LVs, LPs, State).
 
-%clpbn_finalize_solver(State) :-
-%	solver(bp), !,
-%	functor(State, _, Last),
-%	arg(Last, State, Info),
-%	finalize_horus_ground_solver(Info).
-clpbn_finalize_solver(_State).
-
 %
 % This is a routine to start a solver, called by the learning procedures (ie, em).
 %
-
 pfl_init_solver(QueryKeys, AllKeys, Factors, Evidence, State) :-
 	em_solver(Solver),
 	(lifted_solver(Solver) ->
@@ -729,6 +721,11 @@ pfl_run_solver(LVs, LPs, State, bp) :- !,
 pfl_run_solver(LVs, LPs, State, cbp) :- !,
 	run_horus_ground_solver(LVs, LPs, State).
 
+pfl_end_solver(State) :-
+	(em_solver(hve) ; em_solver(bp) ; em_solver(cbp)),
+	finalize_horus_ground_solver(State).
+pfl_end_solver(_State).
+
 
 add_keys(Key1+V1,_Key2,Key1+V1).
 
diff --git a/packages/CLPBN/learning/em.yap b/packages/CLPBN/learning/em.yap
index 0e28d3136..8abe4e92d 100644
--- a/packages/CLPBN/learning/em.yap
+++ b/packages/CLPBN/learning/em.yap
@@ -13,9 +13,9 @@
 		[clpbn_init_graph/1,
 		 clpbn_init_solver/4,
 		 clpbn_run_solver/3,
-		 clpbn_finalize_solver/1,
 		 pfl_init_solver/5,
 		 pfl_run_solver/3,
+		 pfl_end_solver/1,
 		 conditional_probability/3,
 		 clpbn_flag/2
 		]).
@@ -75,7 +75,7 @@
 em(Items, MaxError, MaxIts, Tables, Likelihood) :-
 	catch(init_em(Items, State),Error,handle_em(Error)),
 	em_loop(0, 0.0, State, MaxError, MaxIts, Likelihood, Tables),
-	clpbn_finalize_solver(State),
+	end_em(State),
 	assert(em_found(Tables, Likelihood)),
 	fail.
 % get rid of new random variables the easy way :)
@@ -89,6 +89,12 @@ handle_em(error(repeated_parents)) :- !,
 handle_em(Error) :-
 	throw(Error).
 
+
+end_em(state(_AllDists, _AllDistInstances, _MargKeys, SolverState)) :-
+	clpbn:use_parfactors(on), !,
+	pfl_end_solver(SolverState).
+end_em(_).
+
 % This gets you an initial configuration. If there is a lot of evidence
 % tables may be filled in close to optimal, otherwise they may be
 % close to uniform.