diff --git a/baselines/IPPO/ippo_rnn_smax_split_networks.py b/baselines/IPPO/ippo_rnn_smax_split_networks.py
index b90c8070..f05b5721 100644
--- a/baselines/IPPO/ippo_rnn_smax_split_networks.py
+++ b/baselines/IPPO/ippo_rnn_smax_split_networks.py
@@ -1,5 +1,8 @@
 """
-Based on PureJaxRL Implementation of PPO
+Based on PureJaxRL Implementation of PPO.
+
+NOTE: ppo loss functions currently using `done` masking when computing means.
+
 """
 
 import jax
@@ -102,7 +105,7 @@ def __call__(self, hidden, x):
         rnn_in = (embedding, dones)
         hidden, embedding = ScannedRNN()(hidden, rnn_in)
 
-        critic = nn.Dense(128, kernel_init=orthogonal(2), bias_init=constant(0.0))(
+        critic = nn.Dense(self.config["GRU_HIDDEN_DIM"], kernel_init=orthogonal(2), bias_init=constant(0.0))(
             embedding
         )
         critic = nn.relu(critic)
@@ -297,10 +300,7 @@ def _env_step(runner_state: RNNRunnerState, unused):
                 episode_done = done["__all__"]
                 done_batch = batchify(done, env.agents, config["NUM_ACTORS"]).squeeze()
                 
-                ## term - mask out timesteps when the agent didn't act
-                # if agent was done in the last timestep and the episode is not over then this action is invalid
-                # but if the episode ends, then this action is valid 
-                
+                ## term - mask out timesteps when the agent didn't act                
                 last_done = last_done.reshape((config["NUM_ENVS"], -1))
                 last_ep_done = jnp.all(last_done, axis=1)
                 term = last_done & ~last_ep_done[:, None]
@@ -316,7 +316,7 @@ def _env_step(runner_state: RNNRunnerState, unused):
                     obs_batch,
                     info,
                     avail_actions,
-                    term
+                    term,
                 )
                 
                 hstates = HiddenStates(actor_hstate, critic_hstate)
@@ -340,7 +340,6 @@ def _env_step(runner_state: RNNRunnerState, unused):
             _, last_val = critic_network.apply(params.critic_params, hstates.critic_hstate, ac_in)
             last_val = last_val.squeeze()  # mava here masks out the terminal states but surely unnecessary?
             last_val = jnp.where(last_done, jnp.zeros_like(last_val), last_val)
-            # TODO mava's masking
             
             def _calculate_gae(traj_batch, last_val):
                 def _get_advantages(gae_and_next_value, transition):
@@ -393,7 +392,7 @@ def _actor_loss_fn(
 
                         # CALCULATE ACTOR LOSS
                         ratio = jnp.exp(log_prob - traj_batch.log_prob)
-                        gae = (gae - gae.mean()) / (gae.std() + 1e-8)
+                        gae = (gae - gae.mean()) / (gae.std() + 1e-8)  # NOTE should we also done mask this mean and std..?
                         loss_actor1 = ratio * gae
                         loss_actor2 = (
                             jnp.clip(
@@ -404,7 +403,7 @@ def _actor_loss_fn(
                             * gae
                         )
                         # TODO add back in done masking on the mean 
-                        print('loss actor shape', loss_actor1.shape, 'term shape', traj_batch.terminated.shape)
+                        # print('loss actor shape', loss_actor1.shape, 'term shape', traj_batch.terminated.shape)
                         loss_actor = -jnp.minimum(loss_actor1, loss_actor2)
                         loss_actor = loss_actor.mean(where=(1-traj_batch.done))
                         entropy = pi.entropy().mean(where=(1-traj_batch.done))
@@ -483,9 +482,6 @@ def _critic_loss_fn(critic_params: FrozenDict,
                 rng, shuffle_rng = jax.random.split(rng)
 
                 # adding an additional "fake" dimensionality to perform minibatching correctly
-                # NOTE: mava does this differently, reshaping into "recurrent chunk size" and then permuting
-                # TODO: look at the shapes of mava and jaxmarl
-                # NOTE: mava also handles hstates differently, use the one stored in traj_batch
                 init_hstates = jax.tree_map(
                     lambda x: jnp.reshape(
                         x, (1, config["NUM_ACTORS"], -1)
@@ -499,8 +495,7 @@ def _critic_loss_fn(critic_params: FrozenDict,
                     targets.squeeze(),
                 )
                 permutation = jax.random.permutation(shuffle_rng, config["NUM_ACTORS"])
-                print('batch advantage shape', batch[2].shape)
-                # advantage shape is (num_steps, num_actors)
+
                 shuffled_batch = jax.tree_util.tree_map(
                     lambda x: jnp.take(x, permutation, axis=1), batch
                 )
@@ -517,8 +512,6 @@ def _critic_loss_fn(critic_params: FrozenDict,
                     ),
                     shuffled_batch,
                 )
-                print('minibatch advantage shape', minibatches[2].shape)
-                # advantage shape is (num_minibatches, num_steps, -1)
                 
                 (params, opt_states), loss_info = jax.lax.scan(
                     _update_minbatch, (params, opt_states), minibatches