diff --git a/environments/instruction_following_algorithm_environment.py b/environments/instruction_following_algorithm_environment.py
index c59f43ec..61a9ae6d 100644
--- a/environments/instruction_following_algorithm_environment.py
+++ b/environments/instruction_following_algorithm_environment.py
@@ -745,9 +745,7 @@ class InstructionFollowingEnv(BaseEnv):
 
         await super().close()
 
-    async def score(
-        self, rollout_group_data: List[Dict]
-    ) -> Optional[ScoredDataGroup]:
+    async def score(self, rollout_group_data: List[Dict]) -> Optional[ScoredDataGroup]:
         # rollout_group_data is a list of dicts with messages, answer_info, tokens, masks, logprobs
         # answer_info_dict = {"func_name": ..., "args": ...}
 
@@ -772,7 +770,9 @@ class InstructionFollowingEnv(BaseEnv):
 
         for trajectory_item in rollout_group_data:
             full_trajectory_messages = trajectory_item["messages"]
-            answer_info = trajectory_item["answer_info"]  # {"func_name": ..., "args": ...}
+            answer_info = trajectory_item[
+                "answer_info"
+            ]  # {"func_name": ..., "args": ...}
 
             model_response_text = full_trajectory_messages[-1]["content"]
             func_name = answer_info["func_name"]
@@ -831,7 +831,9 @@ class InstructionFollowingEnv(BaseEnv):
         if current_scores:
             average_score = sum(current_scores) / len(current_scores)
             # Get task info from the first rollout's answer_info
-            answer_info = rollout_group_data[0]["answer_info"] if rollout_group_data else {}
+            answer_info = (
+                rollout_group_data[0]["answer_info"] if rollout_group_data else {}
+            )
             func_name = answer_info.get("func_name", "unknown_task")
 
             # Check if group is too easy for training (but still allow data dumping)