diff --git a/env/wrappers/vec_wrapper.py b/env/wrappers/vec_wrapper.py
index be6e6b5..10cf167 100644
--- a/env/wrappers/vec_wrapper.py
+++ b/env/wrappers/vec_wrapper.py
@@ -9,6 +9,21 @@
 
 
 def _worker(remote, parent_remote, env_fn_wrapper):
+    """
+    Worker process for handling environment interactions in a subprocess.
+
+    Reward computation:
+        - The reward at each step comes directly from env.step(action), which returns
+          (obs, reward, done, truncated, info). The truncated flag is ignored here.
+        - The per-step reward is determined by the underlying environment's reward function.
+        - Rewards are accumulated over the episode in a list. When done=True,
+          the episode_return (sum of all step rewards) is stored in info["episode_return"].
+        - The per-step reward is sent back to the main process via remote.send().
+
+    For ASID (Automatic System Identification), an alternative reward can be computed
+    using ASIDRewardWrapper which calculates reward = trace(J_transpose * J), where J is
+    the Jacobian of observations w.r.t. physics parameters estimated via finite differences.
+    """
     parent_remote.close()
     env = _patch_env(env_fn_wrapper.var())
     # env = env_fn_wrapper.var()
@@ -18,12 +33,15 @@ def _worker(remote, parent_remote, env_fn_wrapper):
             cmd, data = remote.recv()
             # gym interface
             if cmd == "step":
+                # Reward is computed by the underlying environment's step function.
+                # For standard RL tasks, this is the task-specific reward.
+                # For ASID, use ASIDRewardWrapper to compute information-theoretic rewards.
                 obs, reward, done, _, info = env.step(data)
-                rewards.append(reward)
+                rewards.append(reward)  # Accumulate per-step rewards
                 successes.append(info.get("success", 0))
                 if done:
                     info["terminal_obs"] = obs
-                    info["episode_return"] = sum(rewards)
+                    info["episode_return"] = sum(rewards)  # Total episode reward
                     info["episode_success"] = float(sum(successes) > 0)
                     rewards, successes = [], []
                     obs, _ = env.reset()