diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 6f23a08b5..645e5d9e6 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -416,7 +416,9 @@ cdef class Parser:
         free(scores)
         free(token_ids)
 
-    def update(self, docs_tokvecs, golds, drop=0., sgd=None):
+    def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
+        if losses is not None and self.name not in losses:
+            losses[self.name] = 0.
         docs, tokvec_lists = docs_tokvecs
         tokvecs = self.model[0].ops.flatten(tokvec_lists)
         if isinstance(docs, Doc) and isinstance(golds, GoldParse):
@@ -436,18 +438,20 @@ cdef class Parser:
         backprops = []
         d_tokvecs = state2vec.ops.allocate(tokvecs.shape)
         cdef float loss = 0.
-        while len(todo) >= 3:
+        while len(todo) >= 2:
             states, golds = zip(*todo)
 
             token_ids = self.get_token_ids(states)
             vector, bp_vector = state2vec.begin_update(token_ids, drop=0.0)
-            mask = vec2scores.ops.get_dropout_mask(vector.shape, drop)
-            vector *= mask
+            if drop != 0:
+                mask = vec2scores.ops.get_dropout_mask(vector.shape, drop)
+                vector *= mask
             scores, bp_scores = vec2scores.begin_update(vector, drop=drop)
 
             d_scores = self.get_batch_loss(states, golds, scores)
             d_vector = bp_scores(d_scores, sgd=sgd)
-            d_vector *= mask
+            if drop != 0:
+                d_vector *= mask
 
             if isinstance(self.model[0].ops, CupyOps) \
             and not isinstance(token_ids, state2vec.ops.xp.ndarray):
@@ -461,10 +465,12 @@ cdef class Parser:
                 backprops.append((token_ids, d_vector, bp_vector))
             self.transition_batch(states, scores)
             todo = [st for st in todo if not st[0].is_final()]
-            if len(backprops) >= 50:
+            if len(backprops) >= 20:
                 self._make_updates(d_tokvecs,
                     backprops, sgd, cuda_stream)
                 backprops = []
+            if losses is not None:
+                losses[self.name] += (d_scores**2).sum()
         if backprops:
             self._make_updates(d_tokvecs,
                 backprops, sgd, cuda_stream)