diff --git a/src/lerobot/envs/configs.py b/src/lerobot/envs/configs.py
index f815ca3b3..c9db0979f 100644
--- a/src/lerobot/envs/configs.py
+++ b/src/lerobot/envs/configs.py
@@ -14,7 +14,7 @@
 
 import abc
 from dataclasses import dataclass, field
-from typing import Any, Optional
+from typing import Any
 
 import draccus
 
@@ -30,8 +30,6 @@ class EnvConfig(draccus.ChoiceRegistry, abc.ABC):
     fps: int = 30
     features: dict[str, PolicyFeature] = field(default_factory=dict)
     features_map: dict[str, str] = field(default_factory=dict)
-    multitask_eval: bool = False
-    max_parallel_tasks: int = 5
 
     @property
     def type(self) -> str:
@@ -46,7 +44,7 @@ class EnvConfig(draccus.ChoiceRegistry, abc.ABC):
 @EnvConfig.register_subclass("aloha")
 @dataclass
 class AlohaEnv(EnvConfig):
-    task: str = "AlohaInsertion-v0"
+    task: str | None = "AlohaInsertion-v0"
     fps: int = 50
     episode_length: int = 400
     obs_type: str = "pixels_agent_pos"
@@ -84,7 +82,7 @@ class AlohaEnv(EnvConfig):
 @EnvConfig.register_subclass("pusht")
 @dataclass
 class PushtEnv(EnvConfig):
-    task: str = "PushT-v0"
+    task: str | None = "PushT-v0"
     fps: int = 10
     episode_length: int = 300
     obs_type: str = "pixels_agent_pos"
@@ -126,7 +124,7 @@ class PushtEnv(EnvConfig):
 @EnvConfig.register_subclass("xarm")
 @dataclass
 class XarmEnv(EnvConfig):
-    task: str = "XarmLift-v0"
+    task: str | None = "XarmLift-v0"
     fps: int = 15
     episode_length: int = 200
     obs_type: str = "pixels_agent_pos"
@@ -181,10 +179,10 @@ class EnvTransformConfig:
     add_joint_velocity_to_observation: bool = False
     add_current_to_observation: bool = False
     add_ee_pose_to_observation: bool = False
-    crop_params_dict: Optional[dict[str, tuple[int, int, int, int]]] = None
-    resize_size: Optional[tuple[int, int]] = None
+    crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None
+    resize_size: tuple[int, int] | None = None
     control_time_s: float = 20.0
-    fixed_reset_joint_positions: Optional[Any] = None
+    fixed_reset_joint_positions: Any | None = None
     reset_time_s: float = 5.0
     use_gripper: bool = True
     gripper_quantization_threshold: float | None = 0.8
@@ -197,24 +195,25 @@ class EnvTransformConfig:
 class HILSerlRobotEnvConfig(EnvConfig):
     """Configuration for the HILSerlRobotEnv environment."""
 
-    robot: Optional[RobotConfig] = None
-    teleop: Optional[TeleoperatorConfig] = None
-    wrapper: Optional[EnvTransformConfig] = None
+    robot: RobotConfig | None = None
+    teleop: TeleoperatorConfig | None = None
+    wrapper: EnvTransformConfig | None = None
     fps: int = 10
     name: str = "real_robot"
-    mode: str = None  # Either "record", "replay", None
-    repo_id: Optional[str] = None
-    dataset_root: Optional[str] = None
-    task: str = ""
+    mode: str | None = None  # Either "record", "replay", None
+    repo_id: str | None = None
+    dataset_root: str | None = None
+    task: str | None = ""
     num_episodes: int = 10  # only for record mode
     episode: int = 0
     device: str = "cuda"
     push_to_hub: bool = True
-    pretrained_policy_name_or_path: Optional[str] = None
-    reward_classifier_pretrained_path: Optional[str] = None
+    pretrained_policy_name_or_path: str | None = None
+    reward_classifier_pretrained_path: str | None = None
     # For the reward classifier, to record more positive examples after a success
     number_of_steps_after_success: int = 0
 
+    @property
     def gym_kwargs(self) -> dict:
         return {}
 
@@ -224,9 +223,8 @@ class HILSerlRobotEnvConfig(EnvConfig):
 class HILEnvConfig(EnvConfig):
     """Configuration for the HIL environment."""
 
-    type: str = "hil"
     name: str = "PandaPickCube"
-    task: str = "PandaPickCubeKeyboard-v0"
+    task: str | None = "PandaPickCubeKeyboard-v0"
     use_viewer: bool = True
     gripper_penalty: float = 0.0
     use_gamepad: bool = True
@@ -250,18 +248,18 @@ class HILEnvConfig(EnvConfig):
         }
     )
     ################# args from hilserlrobotenv
-    reward_classifier_pretrained_path: Optional[str] = None
-    robot_config: Optional[RobotConfig] = None
-    teleop_config: Optional[TeleoperatorConfig] = None
-    wrapper: Optional[EnvTransformConfig] = None
-    mode: str = None  # Either "record", "replay", None
-    repo_id: Optional[str] = None
-    dataset_root: Optional[str] = None
+    reward_classifier_pretrained_path: str | None = None
+    robot_config: RobotConfig | None = None
+    teleop_config: TeleoperatorConfig | None = None
+    wrapper: EnvTransformConfig | None = None
+    mode: str | None = None  # Either "record", "replay", None
+    repo_id: str | None = None
+    dataset_root: str | None = None
     num_episodes: int = 10  # only for record mode
     episode: int = 0
     device: str = "cuda"
     push_to_hub: bool = True
-    pretrained_policy_name_or_path: Optional[str] = None
+    pretrained_policy_name_or_path: str | None = None
     # For the reward classifier, to record more positive examples after a success
     number_of_steps_after_success: int = 0
     ############################
@@ -274,7 +272,6 @@ class HILEnvConfig(EnvConfig):
             "gripper_penalty": self.gripper_penalty,
         }
 
-
 @EnvConfig.register_subclass("libero")
 @dataclass
 class LiberoEnv(EnvConfig):