diff --git a/cython/gtsam/tests/test_logging_optimizer.py b/cython/gtsam/tests/test_logging_optimizer.py
index 69665db65..2560a72a2 100644
--- a/cython/gtsam/tests/test_logging_optimizer.py
+++ b/cython/gtsam/tests/test_logging_optimizer.py
@@ -43,6 +43,11 @@ class TestOptimizeComet(GtsamTestCase):
         self.optimizer = gtsam.GaussNewtonOptimizer(
             graph, initial, self.params)
 
+        self.lmparams = gtsam.LevenbergMarquardtParams()
+        self.lmoptimizer = gtsam.LevenbergMarquardtOptimizer(
+            graph, initial, self.lmparams
+        )
+
         # setup output capture
         self.capturedOutput = StringIO()
         sys.stdout = self.capturedOutput
@@ -65,6 +70,16 @@ class TestOptimizeComet(GtsamTestCase):
         actual = self.optimizer.values()
         self.gtsamAssertEquals(actual.atRot3(KEY), self.expected, tol=1e-6)
 
+    def test_lm_simple_printing(self):
+        """Make sure we are properly terminating LM"""
+        def hook(_, error):
+            print(error)
+
+        gtsam_optimize(self.lmoptimizer, self.lmparams, hook)
+
+        actual = self.lmoptimizer.values()
+        self.gtsamAssertEquals(actual.atRot3(KEY), self.expected, tol=1e-6)
+
     @unittest.skip("Not a test we want run every time, as needs comet.ml account")
     def test_comet(self):
         """Test with a comet hook."""
diff --git a/cython/gtsam/utils/logging_optimizer.py b/cython/gtsam/utils/logging_optimizer.py
index a48413212..27b9b3a3a 100644
--- a/cython/gtsam/utils/logging_optimizer.py
+++ b/cython/gtsam/utils/logging_optimizer.py
@@ -46,6 +46,7 @@ def gtsam_optimize(optimizer,
     def check_convergence(optimizer, current_error, new_error):
         return (optimizer.iterations() >= params.getMaxIterations()) or (
             gtsam.checkConvergence(params.getRelativeErrorTol(), params.getAbsoluteErrorTol(), params.getErrorTol(),
-                                   current_error, new_error)) or (optimizer.lambda_() > params.getlambdaUpperBound())
+                                   current_error, new_error)) or (
+            type(optimizer).__name__ == "LevenbergMarquardtOptimizer" and optimizer.lambda_() > params.getlambdaUpperBound())
     optimize(optimizer, check_convergence, hook)
     return optimizer.values()