diff --git a/qdax/tasks/brax_envs.py b/qdax/tasks/brax_envs.py
index 6a56ccd5..25c89f3d 100644
--- a/qdax/tasks/brax_envs.py
+++ b/qdax/tasks/brax_envs.py
@@ -149,9 +149,9 @@ def scoring_function_brax_envs(
         generate_unroll,
         episode_length=episode_length,
         play_step_fn=play_step_fn,
-        key=key,
     )
-    _, data = jax.vmap(unroll_fn)(init_states, policies_params)
+    keys = jax.random.split(key, jax.tree.leaves(policies_params)[0].shape[0])
+    _, data = jax.vmap(unroll_fn)(init_states, policies_params, keys)
 
     # Create a mask to extract data properly
     mask = get_mask_from_transitions(data)
diff --git a/qdax/tasks/jumanji_envs.py b/qdax/tasks/jumanji_envs.py
index d390d67a..ed4c4580 100644
--- a/qdax/tasks/jumanji_envs.py
+++ b/qdax/tasks/jumanji_envs.py
@@ -156,25 +156,21 @@ def jumanji_scoring_function(
     When the init states are different, this is not purely stochastic.
     """
 
-    # Perform rollouts with each policy
-    key, subkey = jax.random.split(key)
+    # Step environments
     unroll_fn = partial(
         generate_jumanji_unroll,
         episode_length=episode_length,
         play_step_fn=play_step_fn,
-        key=subkey,
     )
+    keys = jax.random.split(key, jax.tree.leaves(policies_params)[0].shape[0])
+    _, _, data = jax.vmap(unroll_fn)(init_states, init_timesteps, policies_params, keys)
 
-    _final_state, _final_timestep, data = jax.vmap(unroll_fn)(
-        init_states, init_timesteps, policies_params
-    )
-
-    # create a mask to extract data properly
+    # Create a mask to extract data properly
     is_done = jnp.clip(jnp.cumsum(data.dones, axis=1), 0, 1)
     mask = jnp.roll(is_done, 1, axis=1)
     mask = mask.at[:, 0].set(0)
 
-    # scores
+    # Evaluate
     fitnesses = jnp.sum(data.rewards * (1.0 - mask), axis=1)
     descriptors = descriptor_extractor(data, mask)