diff --git a/spacy/syntax/beam_parser.pyx b/spacy/syntax/beam_parser.pyx
index 6cab37cba..6ea93abc9 100644
--- a/spacy/syntax/beam_parser.pyx
+++ b/spacy/syntax/beam_parser.pyx
@@ -1,5 +1,6 @@
 # cython: profile=True
 # cython: experimental_cpp_class_def=True
+# cython: cdivision=True
 """
 MALT-style dependency parser
 """
@@ -11,7 +12,7 @@ from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
 from libc.stdint cimport uint32_t, uint64_t
 from libc.string cimport memset, memcpy
 from libc.stdlib cimport rand
-from libc.math cimport log, exp
+from libc.math cimport log, exp, isnan, isinf
 import random
 import os.path
 from os import path
@@ -67,7 +68,7 @@ def get_templates(name):
 
 
 cdef int BEAM_WIDTH = 8
-
+MAX_VIOLN_UPDATE = False
 
 cdef class BeamParser(Parser):
     cdef public int beam_width
@@ -91,7 +92,7 @@ cdef class BeamParser(Parser):
             tokens[i] = state.c._sent[i]
         _cleanup(beam)
 
-    def train(self, Doc tokens, GoldParse gold_parse):
+    def train(self, Doc tokens, GoldParse gold_parse, itn=0):
         self.moves.preprocess_gold(gold_parse)
         cdef Beam pred = Beam(self.moves.n_moves, self.beam_width)
         pred.initialize(_init_state, tokens.length, tokens.c)
@@ -100,6 +101,7 @@ cdef class BeamParser(Parser):
         cdef Beam gold = Beam(self.moves.n_moves, self.beam_width)
         gold.initialize(_init_state, tokens.length, tokens.c)
         gold.check_done(_check_final_state, NULL)
+        violn = MaxViolation()
         while not pred.is_done and not gold.is_done:
             # We search separately here, to allow for ambiguity in the gold
             # parse.
@@ -107,38 +109,49 @@ cdef class BeamParser(Parser):
             self._advance_beam(gold, gold_parse, True)
             if MAX_VIOLN_UPDATE:
                 violn.check_crf(pred, gold)
+                if violn.delta >= 10000:
+                    break
             elif pred.min_score > gold.score: # Early update
                 break
-        cdef long double Z = 0.0
         if MAX_VIOLN_UPDATE:
-            if violn.delta != -1:
-                for prob, hist in zip(violn.p_scores, violn.p_hist):
-                    self._update_dense(tokens, hist, prob / violn.Z)
-                for prob, hist in zip(violn.g_scores, violn.g_hist):
-                    self._update_dense(tokens, hist, -prob / violn.gZ)
+            self._max_violation_update(
+                tokens, violn.p_probs, violn.p_hist,
+                violn.g_probs, violn.g_hist)
         else:
-            # Gather the partition function --- Z --- by which we can normalize the
-            # scores into a probability distribution. The simple idea here is that
-            # we clip the probability of all parses outside the beam to 0.
-            for i in range(pred.size):
-                # Make sure we've only got negative examples here.
-                # Otherwise, we might double-count the gold.
-                if pred._states[i].loss > 0: 
-                    Z += exp(pred._states[i].score)
-            if Z > 0: # If no negative examples, don't update.
-                Z += exp(gold.score)
-                for i, hist in enumerate(pred.histories):
-                    if pred._states[i].loss > 0:
-                        # Update with the negative example.
-                        # Gradient of loss is P(parse) - 0
-                        self._update_dense(tokens, hist, exp(pred._states[i].score) / Z)
-                # Update with the positive example.
-                # Gradient of loss is P(parse) - 1
-                self._update_dense(tokens, gold.histories[0], (exp(gold.score) / Z) - 1)
+            self._early_update(tokens, pred, gold)
         _cleanup(pred)
         _cleanup(gold)
         return pred.loss
 
+    def _max_violation_update(self, Doc doc, p_grads, p_hist, g_grads, g_hist):
+        for grad, hist in zip(p_grads, p_hist):
+            if abs(grad) >= 1e-5:
+                self._update_dense(doc, hist, grad)
+        for grad, hist in zip(g_grads, g_hist):
+            if abs(grad) >= 1e-5:
+                self._update_dense(doc, hist, -grad)
+
+    def _early_update(self, Doc doc, Beam pred, Beam gold):
+        # Gather the partition function --- Z --- by which we can normalize the
+        # scores into a probability distribution. The simple idea here is that
+        # we clip the probability of all parses outside the beam to 0.
+        cdef long double Z = 0.0
+        for i in range(pred.size):
+            # Make sure we've only got negative examples here.
+            # Otherwise, we might double-count the gold.
+            if pred._states[i].loss > 0: 
+                Z += exp(pred._states[i].score)
+        if Z > 0: # If no negative examples, don't update.
+            Z += exp(gold.score)
+            for i, hist in enumerate(pred.histories):
+                if pred._states[i].loss > 0:
+                    # Update with the negative example.
+                    # Gradient of loss is P(parse) - 0
+                    self._update_dense(doc, hist, exp(pred._states[i].score) / Z)
+            # Update with the positive example.
+            # Gradient of loss is P(parse) - 1
+            self._update_dense(doc, gold.histories[0], (exp(gold.score) / Z) - 1)
+
     def _advance_beam(self, Beam beam, GoldParse gold, bint follow_gold):
         cdef Example py_eg = Example(nr_class=self.moves.n_moves, nr_atom=CONTEXT_SIZE,
                                      nr_feat=self.model.nr_feat, widths=self.model.widths)