diff --git a/gtsam/nonlinear/GncOptimizer.h b/gtsam/nonlinear/GncOptimizer.h
index 3025d24681..cc3fdaf34f 100644
--- a/gtsam/nonlinear/GncOptimizer.h
+++ b/gtsam/nonlinear/GncOptimizer.h
@@ -184,7 +184,8 @@ class GTSAM_EXPORT GncOptimizer {
   /// Compute optimal solution using graduated non-convexity.
   Values optimize() {
     NonlinearFactorGraph graph_initial = this->makeWeightedGraph(weights_);
-    BaseOptimizer baseOptimizer(graph_initial, state_);
+    BaseOptimizer baseOptimizer(
+        graph_initial, state_, params_.baseOptimizerParams);
     Values result = baseOptimizer.optimize();
     double mu = initializeMu();
     double prev_cost = graph_initial.error(result);
@@ -228,7 +229,8 @@ class GTSAM_EXPORT GncOptimizer {
 
       // variable/values update
       NonlinearFactorGraph graph_iter = this->makeWeightedGraph(weights_);
-      BaseOptimizer baseOptimizer_iter(graph_iter, state_);
+      BaseOptimizer baseOptimizer_iter(
+          graph_iter, state_, params_.baseOptimizerParams);
       result = baseOptimizer_iter.optimize();
 
       // stopping condition
diff --git a/tests/testGncOptimizer.cpp b/tests/testGncOptimizer.cpp
index a3d1e4e9b0..c3335ce20c 100644
--- a/tests/testGncOptimizer.cpp
+++ b/tests/testGncOptimizer.cpp
@@ -98,6 +98,30 @@ TEST(GncOptimizer, gncConstructor) {
   CHECK(gnc.equals(gnc2));
 }
 
+/* ************************************************************************* */
+TEST(GncOptimizer, solverParameterParsing) {
+  // has to have Gaussian noise models !
+  auto fg = example::createReallyNonlinearFactorGraph();  // just a unary factor
+                                                          // on a 2D point
+
+  Point2 p0(3, 3);
+  Values initial;
+  initial.insert(X(1), p0);
+
+  LevenbergMarquardtParams lmParams;
+  lmParams.setMaxIterations(0); // forces not to perform optimization
+  GncParams<LevenbergMarquardtParams> gncParams(lmParams);
+  auto gnc = GncOptimizer<GncParams<LevenbergMarquardtParams>>(fg, initial,
+                                                               gncParams);
+  Values result = gnc.optimize();
+
+  // check that LM did not perform optimization and result is the same as the initial guess
+  DOUBLES_EQUAL(fg.error(initial), fg.error(result), tol);
+
+  // also check the params:
+  DOUBLES_EQUAL(0.0, gncParams.baseOptimizerParams.maxIterations, tol);
+}
+
 /* ************************************************************************* */
 TEST(GncOptimizer, gncConstructorWithRobustGraphAsInput) {
   auto fg = example::sharedNonRobustFactorGraphWithOutliers();