diff --git a/spacy/language.py b/spacy/language.py
index 70dad59f3..c72467ae7 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -1166,14 +1166,20 @@ class Language:
         if not hasattr(get_examples, "__call__"):
             err = Errors.E930.format(name="Language", obj=type(get_examples))
             raise ValueError(err)
+        valid_examples = False
         for example in get_examples():
             if not isinstance(example, Example):
                 err = Errors.E978.format(
                     name="Language.begin_training", types=type(example)
                 )
                 raise ValueError(err)
+            else:
+                valid_examples = True
             for word in [t.text for t in example.reference]:
                 _ = self.vocab[word]  # noqa: F841
+        if not valid_examples:
+            err = Errors.E930.format(name="Language", obj="empty list")
+            raise ValueError(err)
         if device >= 0:  # TODO: do we need this here?
             require_gpu(device)
             if self.vocab.vectors.data.shape[1] >= 1:
diff --git a/spacy/training/corpus.py b/spacy/training/corpus.py
index 545f01eaa..8f26b0ed2 100644
--- a/spacy/training/corpus.py
+++ b/spacy/training/corpus.py
@@ -47,7 +47,7 @@ class Corpus:
         *,
         limit: int = 0,
         gold_preproc: bool = False,
-        max_length: bool = False,
+        max_length: int = 0,
     ) -> None:
         self.path = util.ensure_path(path)
         self.gold_preproc = gold_preproc
@@ -89,7 +89,7 @@ class Corpus:
         if self.gold_preproc:
             examples = self.make_examples_gold_preproc(nlp, ref_docs)
         else:
-            examples = self.make_examples(nlp, ref_docs, self.max_length)
+            examples = self.make_examples(nlp, ref_docs)
         yield from examples
 
     def _make_example(
@@ -108,18 +108,18 @@ class Corpus:
             return Example(nlp.make_doc(reference.text), reference)
 
     def make_examples(
-        self, nlp: "Language", reference_docs: Iterable[Doc], max_length: int = 0
+        self, nlp: "Language", reference_docs: Iterable[Doc]
     ) -> Iterator[Example]:
         for reference in reference_docs:
             if len(reference) == 0:
                 continue
-            elif max_length == 0 or len(reference) < max_length:
+            elif self.max_length == 0 or len(reference) < self.max_length:
                 yield self._make_example(nlp, reference, False)
             elif reference.is_sentenced:
                 for ref_sent in reference.sents:
                     if len(ref_sent) == 0:
                         continue
-                    elif max_length == 0 or len(ref_sent) < max_length:
+                    elif self.max_length == 0 or len(ref_sent) < self.max_length:
                         yield self._make_example(nlp, ref_sent.as_doc(), False)
 
     def make_examples_gold_preproc(