diff --git a/spacy/pipeline/coref.py b/spacy/pipeline/coref.py
index 25a353405..1c0e56521 100644
--- a/spacy/pipeline/coref.py
+++ b/spacy/pipeline/coref.py
@@ -503,29 +503,20 @@ class SpanPredictor(TrainablePipe):
             losses = {}
         losses.setdefault(self.name, 0.0)
         validate_examples(examples, "SpanPredictor.update")
-        if not any(len(eg.predicted) if eg.predicted else 0 for eg in examples):
+        if not any(len(eg.reference) if eg.reference else 0 for eg in examples):
             # Handle cases where there are no tokens in any docs.
             return losses
         set_dropout_rate(self.model, drop)
 
         total_loss = 0
         for eg in examples:
-            # replicates the EntityLinker's behaviour and
-            # copies annotations over https://bit.ly/3iweDcW
-            # https://github.com/explosion/spaCy/blob/master/spacy/pipeline/entity_linker.py#L313
-            doc = eg.predicted
-            old_spans = eg.predicted.spans
-            for key, sg in eg.reference.spans.items():
-                if key.startswith(self.input_prefix):
-                    doc.spans[key] = eg.get_aligned_spans_y2x(sg)
-            span_scores, backprop = self.model.begin_update([doc])
+            # For update we use the gold coref_head_clusters
+            # in the reference.
+            span_scores, backprop = self.model.begin_update([eg.reference])
             loss, d_scores = self.get_loss([eg], span_scores)
             total_loss += loss
             # TODO check shape here
             backprop(d_scores)
-            # Restore example
-            for key, sg in old_spans.items():
-                eg.predicted.spans[key] = sg
 
         if sgd is not None:
             self.finish_update(sgd)
@@ -570,17 +561,14 @@ class SpanPredictor(TrainablePipe):
         # span_scores is a Floats3d. What are the axes? mention x token x start/end
 
         for eg in examples:
-
-            # get gold data
-            gold = doc2clusters(eg.predicted, self.input_prefix)
-            # flatten the gold data
             starts = []
             ends = []
-            for cluster in gold:
-                for mention in cluster:
-                    starts.append(mention[0])
-                    # XXX I think this was missing here
-                    ends.append(mention[1] - 1)
+            for key, sg in eg.reference.spans.items():
+                if key.startswith(self.output_prefix):
+                    for mention in sg:
+                        starts.append(mention.start)
+                        ends.append(mention.end)
+
             starts = self.model.ops.xp.asarray(starts)
             ends = self.model.ops.xp.asarray(ends)
             start_scores = span_scores[:, :, 0]