diff --git a/CHANGELOG.md b/CHANGELOG.md
index 25829b1db..d13ba4e53 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -11,6 +11,13 @@ Note that Sockeye has checks in place to not translate with an old model that wa
 
 Each version section may have have subsections for: _Added_, _Changed_, _Removed_, _Deprecated_, and _Fixed_.
 
+## [2.3.8]
+
+### Fixed
+
+- Fix problem identified in issue #925 that caused learning rate
+  warmup to fail in some instances when doing continued training 
+
 ## [2.3.7]
 
 ### Changed
diff --git a/sockeye/__init__.py b/sockeye/__init__.py
index 94d1a6150..17b45bf0d 100644
--- a/sockeye/__init__.py
+++ b/sockeye/__init__.py
@@ -11,4 +11,4 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
-__version__ = '2.3.7'
+__version__ = '2.3.8'
diff --git a/sockeye/training.py b/sockeye/training.py
index 1a9d5e707..4ef8972e1 100644
--- a/sockeye/training.py
+++ b/sockeye/training.py
@@ -582,6 +582,8 @@ def _load_lr_scheduler(self, fname):
             with open(fname, "rb") as fp:
                 self.trainer.optimizer.lr_scheduler = pickle.load(fp)
             logger.info("Loaded '%s' from '%s'", self.trainer.optimizer.lr_scheduler, fname)
+        self.trainer.optimizer.begin_num_update = self.state.updates
+        self.trainer.optimizer.num_update = self.state.updates
 
     def _save_training_state(self, train_iter: data_io.BaseParallelSampleIter):
         """
@@ -603,10 +605,6 @@ def _save_training_state(self, train_iter: data_io.BaseParallelSampleIter):
         opt_state_fname = os.path.join(training_state_dirname, C.OPT_STATES_LAST)
         self._save_trainer_states(opt_state_fname)
 
-        # (2.5) lr_scheduler
-        lr_scheduler_fname = os.path.join(training_state_dirname, C.LR_SCHEDULER_LAST)
-        self._save_lr_scheduler(lr_scheduler_fname)
-
         # (3) Data iterator
         train_iter.save_state(os.path.join(training_state_dirname, C.BUCKET_ITER_STATE_NAME))
 
@@ -621,6 +619,10 @@ def _save_training_state(self, train_iter: data_io.BaseParallelSampleIter):
         # (5) Training state
         self.state.save(os.path.join(training_state_dirname, C.TRAINING_STATE_NAME))
 
+        # (5.5) lr_scheduler
+        lr_scheduler_fname = os.path.join(training_state_dirname, C.LR_SCHEDULER_LAST)
+        self._save_lr_scheduler(lr_scheduler_fname)
+
         # (6) AMP loss scaler state
         if self.using_amp:
             with open(os.path.join(training_state_dirname, C.AMP_LOSS_SCALER_STATE_NAME), "wb") as fp:
@@ -658,10 +660,6 @@ def _load_training_state(self, train_iter: data_io.BaseParallelSampleIter):
         opt_state_fname = os.path.join(self.training_state_dirname, C.OPT_STATES_LAST)
         self._load_trainer_states(opt_state_fname)
 
-        # (2.5) lr_scheduler
-        lr_scheduler_fname = os.path.join(self.training_state_dirname, C.LR_SCHEDULER_LAST)
-        self._load_lr_scheduler(lr_scheduler_fname)
-
         # (3) Data Iterator
         train_iter.load_state(os.path.join(self.training_state_dirname, C.BUCKET_ITER_STATE_NAME))
 
@@ -676,6 +674,10 @@ def _load_training_state(self, train_iter: data_io.BaseParallelSampleIter):
         # (5) Training state
         self.state = TrainState.load(os.path.join(self.training_state_dirname, C.TRAINING_STATE_NAME))
 
+        # (5.5) lr_scheduler
+        lr_scheduler_fname = os.path.join(self.training_state_dirname, C.LR_SCHEDULER_LAST)
+        self._load_lr_scheduler(lr_scheduler_fname)
+
         # (6) AMP loss scaler state
         if self.using_amp:
             # Load loss scaler state