diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 3411bdc68..e1d5cdefc 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -58,6 +58,7 @@ from .. import about
         str,
     ),
     noise_level=("Amount of corruption for data augmentation", "option", "nl", float),
+    eval_beam_widths=("Beam widths to evaluate, e.g. 4,8", "option", "bw", str),
     gold_preproc=("Use gold preprocessing", "flag", "G", bool),
     learn_tokens=("Make parser learn gold-standard tokenization", "flag", "T", bool),
     verbose=("Display more information for debug", "flag", "VV", bool),
@@ -81,6 +82,7 @@ def train(
     parser_multitasks="",
     entity_multitasks="",
     noise_level=0.0,
+    eval_beam_widths="",
     gold_preproc=False,
     learn_tokens=False,
     verbose=False,
@@ -134,6 +136,14 @@ def train(
         util.env_opt("batch_compound", 1.001),
     )
 
+    if not eval_beam_widths:
+        eval_beam_widths = [1]
+    else:
+        eval_beam_widths = [int(bw) for bw in eval_beam_widths.split(",")]
+        if 1 not in eval_beam_widths:
+            eval_beam_widths.append(1)
+        eval_beam_widths.sort()
+
     # Set up the base model and pipeline. If a base model is specified, load
     # the model and make sure the pipeline matches the pipeline setting. If
     # training starts from a blank model, intitalize the language class.
@@ -247,7 +257,7 @@ def train(
                 epoch_model_path = output_path / ("model%d" % i)
                 nlp.to_disk(epoch_model_path)
                 nlp_loaded = util.load_model_from_path(epoch_model_path)
-                for beam_width in [1, 4, 16, 128]:
+                for beam_width in eval_beam_widths:
                     for name, component in nlp_loaded.pipeline:
                         if hasattr(component, "cfg"):
                             component.cfg["beam_width"] = beam_width