diff --git a/spacy/default_config.cfg b/spacy/default_config.cfg
index 694fb732f..8bd2c7065 100644
--- a/spacy/default_config.cfg
+++ b/spacy/default_config.cfg
@@ -84,6 +84,8 @@ score_weights = {}
 frozen_components = []
 # Names of pipeline components that should set annotations during training
 annotating_components = []
+# Names of pipeline components that should get rehearsed during training
+rehearse_components = []
 # Location in the config where the dev corpus is defined
 dev_corpus = "corpora.dev"
 # Location in the config where the train corpus is defined
diff --git a/spacy/schemas.py b/spacy/schemas.py
index 9bdff3030..cab909e21 100644
--- a/spacy/schemas.py
+++ b/spacy/schemas.py
@@ -356,7 +356,7 @@ class ConfigSchemaTraining(BaseModel):
     logger: Logger = Field(..., title="The logger to track training progress")
     frozen_components: List[str] = Field(..., title="Pipeline components that shouldn't be updated during training")
     annotating_components: List[str] = Field(..., title="Pipeline components that should set annotations during training")
-    rehearse_components: Optional[List[str]] = Field(..., title="Pipeline components that should be rehearsed during training")
+    rehearse_components: List[str] = Field(..., title="Pipeline components that should be rehearsed during training")
     before_to_disk: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after training, before it's saved to disk")
     before_update: Optional[Callable[["Language", Dict[str, Any]], None]] = Field(..., title="Optional callback that is invoked at the start of each training step")
     # fmt: on
diff --git a/spacy/training/initialize.py b/spacy/training/initialize.py
index 2315c1140..1d2184dd1 100644
--- a/spacy/training/initialize.py
+++ b/spacy/training/initialize.py
@@ -60,8 +60,6 @@ def init_nlp(config: Config, *, use_gpu: int = -1) -> "Language":
     optimizer = T["optimizer"]
     # Components that shouldn't be updated during training
     frozen_components = T["frozen_components"]
-    # Components that shouldn't be updated during training
-    rehearse_components = T["rehearse_components"]
     # Sourced components that require resume_training
     resume_components = [p for p in sourced if p not in frozen_components]
     logger.info(f"Pipeline: {nlp.pipe_names}")
@@ -69,6 +67,8 @@ def init_nlp(config: Config, *, use_gpu: int = -1) -> "Language":
         with nlp.select_pipes(enable=resume_components):
             logger.info(f"Resuming training for: {resume_components}")
             nlp.resume_training(sgd=optimizer)
+    # Components that shouldn't be updated during training
+    rehearse_components = T["rehearse_components"]
     if rehearse_components:
         logger.info(f"Rehearsing components: {rehearse_components}")
     # Make sure that listeners are defined before initializing further