diff --git a/libs/langchain/langchain/chains/rl_chain/base.py b/libs/langchain/langchain/chains/rl_chain/base.py
index fb4143f4655..c250815943d 100644
--- a/libs/langchain/langchain/chains/rl_chain/base.py
+++ b/libs/langchain/langchain/chains/rl_chain/base.py
@@ -343,6 +343,7 @@ class RLChain(Chain, Generic[TEvent]):
     selection_scorer: Union[SelectionScorer, None]
     active_policy: Policy = _NoOpPolicy()
     auto_embed: bool = False
+    selection_scorer_activated: bool = True
     selected_input_key = "rl_chain_selected"
     selected_based_on_input_key = "rl_chain_selected_based_on"
     metrics: Optional[MetricsTracker] = None
@@ -400,6 +401,42 @@ class RLChain(Chain, Generic[TEvent]):
         """
         return [self.output_key]
 
+    def update_with_delayed_score(
+        self, score: float, event: TEvent, force_score: bool = False
+    ) -> None:
+        """
+        Updates the learned policy with the score provided.
+        Will raise an error if selection_scorer is set, and force_score=True was not provided during the method call
+        """  # noqa: E501
+        if self._can_use_selection_scorer() and not force_score:
+            raise RuntimeError(
+                "The selection scorer is set, and force_score was not set to True. \
+                    Please set force_score=True to use this function."
+            )
+        if self.metrics:
+            self.metrics.on_feedback(score)
+        self._call_after_scoring_before_learning(event=event, score=score)
+        self.active_policy.learn(event=event)
+        self.active_policy.log(event=event)
+
+    def deactivate_selection_scorer(self) -> None:
+        """
+        Deactivates the selection scorer, meaning that the chain will no longer attempt to use the selection scorer to score responses.
+        """  # noqa: E501
+        self.selection_scorer_activated = False
+
+    def activate_selection_scorer(self) -> None:
+        """
+        Activates the selection scorer, meaning that the chain will attempt to use the selection scorer to score responses.
+        """  # noqa: E501
+        self.selection_scorer_activated = True
+
+    def save_progress(self) -> None:
+        """
+        This function should be called to save the state of the learned policy model.
+        """  # noqa: E501
+        self.active_policy.save()
+
     def _validate_inputs(self, inputs: Dict[str, Any]) -> None:
         super()._validate_inputs(inputs)
         if (
@@ -412,6 +449,12 @@ class RLChain(Chain, Generic[TEvent]):
                         they are reserved for internal use during auto reward."
             )
 
+    def _can_use_selection_scorer(self) -> bool:
+        """
+        Returns whether the chain can use the selection scorer to score responses or not.
+        """  # noqa: E501
+        return self.selection_scorer is not None and self.selection_scorer_activated
+
     @abstractmethod
     def _call_before_predict(self, inputs: Dict[str, Any]) -> TEvent:
         ...
@@ -434,30 +477,6 @@ class RLChain(Chain, Generic[TEvent]):
     ) -> TEvent:
         ...
 
-    def update_with_delayed_score(
-        self, score: float, event: TEvent, force_score: bool = False
-    ) -> None:
-        """
-        Updates the learned policy with the score provided.
-        Will raise an error if selection_scorer is set, and force_score=True was not provided during the method call
-        """  # noqa: E501
-        if self.selection_scorer and not force_score:
-            raise RuntimeError(
-                "The selection scorer is set, and force_score was not set to True. \
-                    Please set force_score=True to use this function."
-            )
-        if self.metrics:
-            self.metrics.on_feedback(score)
-        self._call_after_scoring_before_learning(event=event, score=score)
-        self.active_policy.learn(event=event)
-        self.active_policy.log(event=event)
-
-    def set_auto_embed(self, auto_embed: bool) -> None:
-        """
-        Sets whether the chain should auto embed the inputs or not.
-        """
-        self.auto_embed = auto_embed
-
     def _call(
         self,
         inputs: Dict[str, Any],
@@ -494,8 +513,8 @@ class RLChain(Chain, Generic[TEvent]):
 
         score = None
         try:
-            if self.selection_scorer:
-                score = self.selection_scorer.score_response(
+            if self._can_use_selection_scorer():
+                score = self.selection_scorer.score_response(  # type: ignore
                     inputs=next_chain_inputs, llm_response=output, event=event
                 )
         except Exception as e:
@@ -511,12 +530,6 @@ class RLChain(Chain, Generic[TEvent]):
 
         return {self.output_key: {"response": output, "selection_metadata": event}}
 
-    def save_progress(self) -> None:
-        """
-        This function should be called to save the state of the learned policy model.
-        """
-        self.active_policy.save()
-
     @property
     def _chain_type(self) -> str:
         return "llm_personalizer_chain"
diff --git a/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_chain_call.py b/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_chain_call.py
index 2af08840b5e..d7dee7fdf64 100644
--- a/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_chain_call.py
+++ b/libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_chain_call.py
@@ -363,3 +363,41 @@ def test_calling_chain_w_reserved_inputs_throws() -> None:
             User=rl_chain.BasedOn("Context"),
             rl_chain_selected=rl_chain.ToSelectFrom(["0", "1", "2"]),
         )
+
+
+@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
+def test_activate_and_deactivate_scorer() -> None:
+    llm, PROMPT = setup()
+    scorer_llm = FakeListChatModel(responses=[300])
+    chain = pick_best_chain.PickBest.from_llm(
+        llm=llm,
+        prompt=PROMPT,
+        selection_scorer=pick_best_chain.base.AutoSelectionScorer(llm=scorer_llm),
+        feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
+    )
+    response = chain.run(
+        User=pick_best_chain.base.BasedOn("Context"),
+        action=pick_best_chain.base.ToSelectFrom(["0", "1", "2"]),
+    )
+    # chain llm used for both basic prompt and for scoring
+    assert response["response"] == "hey"
+    selection_metadata = response["selection_metadata"]
+    assert selection_metadata.selected.score == 300.0
+
+    chain.deactivate_selection_scorer()
+    response = chain.run(
+        User=pick_best_chain.base.BasedOn("Context"),
+        action=pick_best_chain.base.ToSelectFrom(["0", "1", "2"]),
+    )
+    assert response["response"] == "hey"
+    selection_metadata = response["selection_metadata"]
+    assert selection_metadata.selected.score is None
+
+    chain.activate_selection_scorer()
+    response = chain.run(
+        User=pick_best_chain.base.BasedOn("Context"),
+        action=pick_best_chain.base.ToSelectFrom(["0", "1", "2"]),
+    )
+    assert response["response"] == "hey"
+    selection_metadata = response["selection_metadata"]
+    assert selection_metadata.selected.score == 300.0