diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py
index 0863bcc578..01a4029705 100755
--- a/gensim/models/ldamodel.py
+++ b/gensim/models/ldamodel.py
@@ -542,6 +542,7 @@ def init_dir_prior(self, prior, name):
         name : {'alpha', 'eta'}
             Whether the `prior` is parameterized by the alpha vector (1 parameter per topic)
             or by the eta (1 parameter per unique term in the vocabulary).
+
         """
         if prior is None:
             prior = 'symmetric'
@@ -609,8 +610,8 @@ def sync_state(self, current_Elogbeta=None):
         current_Elogbeta: numpy.ndarray
             Posterior probabilities for each topic, optional.
             If omitted, it will get Elogbeta from state.
-        """
 
+        """
         if current_Elogbeta is None:
             current_Elogbeta = self.state.get_Elogbeta()
         self.expElogbeta = np.exp(current_Elogbeta)
@@ -1201,7 +1202,6 @@ def show_topic(self, topicid, topn=10):
     def get_topics(self):
         """Get the term-topic matrix learned during inference.
 
-
         Returns
         -------
         numpy.ndarray
diff --git a/gensim/test/test_ldamodel.py b/gensim/test/test_ldamodel.py
index a292ed61d0..b809b39754 100644
--- a/gensim/test/test_ldamodel.py
+++ b/gensim/test/test_ldamodel.py
@@ -13,6 +13,7 @@
 import numbers
 import os
 import unittest
+import copy
 
 import numpy as np
 from numpy.testing import assert_allclose
@@ -41,6 +42,25 @@ def setUp(self):
         self.class_ = ldamodel.LdaModel
         self.model = self.class_(corpus, id2word=dictionary, num_topics=2, passes=100)
 
+    def test_sync_state(self):
+        model2 = self.class_(corpus=self.corpus, id2word=dictionary, num_topics=2, passes=1)
+        model2.state = copy.deepcopy(self.model.state)
+        model2.sync_state()
+
+        assert_allclose(self.model.get_term_topics(2), model2.get_term_topics(2), rtol=1e-5)
+        assert_allclose(self.model.get_topics(), model2.get_topics(), rtol=1e-5)
+
+        # properly continues training on the new state
+        self.model.random_state = np.random.RandomState(0)
+        model2.random_state = np.random.RandomState(0)
+        self.model.passes = 1
+        model2.passes = 1
+        self.model.update(self.corpus)
+        model2.update(self.corpus)
+
+        assert_allclose(self.model.get_term_topics(2), model2.get_term_topics(2), rtol=1e-5)
+        assert_allclose(self.model.get_topics(), model2.get_topics(), rtol=1e-5)
+
     def test_transform(self):
         passed = False
         # sometimes, LDA training gets stuck at a local minimum