diff --git a/amago/cli_utils.py b/amago/cli_utils.py
index 1c30104..7c99156 100644
--- a/amago/cli_utils.py
+++ b/amago/cli_utils.py
@@ -73,7 +73,7 @@ def add_common_cli(parser: ArgumentParser) -> ArgumentParser:
     )
     # main learning schedule
     parser.add_argument(
-        "--grads_per_epoch",
+        "--batches_per_epoch",
         type=int,
         default=1000,
         help="Gradient updates per training epoch.",
@@ -82,7 +82,7 @@ def add_common_cli(parser: ArgumentParser) -> ArgumentParser:
         "--timesteps_per_epoch",
         type=int,
         default=1000,
-        help="Timesteps of environment interaction per epoch *per actor*. The update:data ratio is defined by `grads_per_epoch / (timesteps_per_epoch * parallel_actors)`.",
+        help="Timesteps of environment interaction per epoch *per actor*. The update:data ratio is defined by `batches_per_epoch / (timesteps_per_epoch * parallel_actors)`.",
     )
     parser.add_argument(
         "--val_interval",
@@ -326,7 +326,7 @@ def create_experiment_from_cli(
         epochs=cli.epochs,
         parallel_actors=cli.parallel_actors,
         train_timesteps_per_epoch=cli.timesteps_per_epoch,
-        train_grad_updates_per_epoch=cli.grads_per_epoch,
+        train_batches_per_epoch=cli.batches_per_epoch,
         start_learning_at_epoch=cli.start_learning_at_epoch,
         val_interval=cli.val_interval,
         ckpt_interval=cli.ckpt_interval,
@@ -353,7 +353,7 @@ def make_experiment_learn_only(experiment: amago.Experiment) -> amago.Experiment
 def make_experiment_collect_only(experiment: amago.Experiment) -> amago.Experiment:
     experiment.start_collecting_at_epoch = 0
     experiment.start_learning_at_epoch = float("inf")
-    experiment.train_grad_updates_per_epoch = 0
+    experiment.train_batches_per_epoch = 0
     experiment.val_checks_per_epoch = 0
     experiment.ckpt_interval = None
     experiment.always_save_latest = False
diff --git a/amago/envs/builtin/babyai.py b/amago/envs/builtin/babyai.py
index 9019ac2..8f95a74 100644
--- a/amago/envs/builtin/babyai.py
+++ b/amago/envs/builtin/babyai.py
@@ -10,7 +10,11 @@
 
 from amago.envs import AMAGOEnv
 from amago.hindsight import GoalSeq
-from amago.envs.env_utils import space_convert, DiscreteActionWrapper
+from amago.envs.env_utils import (
+    space_convert,
+    DiscreteActionWrapper,
+    AMAGO_ENV_LOG_PREFIX,
+)
 
 
 BANNED_BABYAI_TASKS = [
@@ -181,6 +185,9 @@ def step(self, action):
         next_obs, reward, terminated, truncated, info = self.env.step(action)
         done = False
         if terminated or truncated:
+            info[f"{AMAGO_ENV_LOG_PREFIX}Episode {self.current_episode} Success"] = (
+                reward > 0.0
+            )
             self.current_episode += 1
             if self.current_episode > self.k_episodes:
                 done = True
diff --git a/amago/envs/builtin/gym_envs.py b/amago/envs/builtin/gym_envs.py
index 6649589..9fc118b 100644
--- a/amago/envs/builtin/gym_envs.py
+++ b/amago/envs/builtin/gym_envs.py
@@ -6,6 +6,7 @@
 import numpy as np
 
 from amago.envs import AMAGOEnv
+from amago.envs.env_utils import AMAGO_ENV_LOG_PREFIX
 from amago.hindsight import GoalSeq
 
 
@@ -130,11 +131,11 @@ def __init__(
 
     def reset(self, *args, **kwargs):
         self.current_map = [[t for t in row] for row in generate_random_map(self.size)]
-        self.action_mapping = [(0, 0), (1, 0), (-1, 0), (0, 1), (0, -1)]
+        act_map = [(0, 0), (1, 0), (-1, 0), (0, 1), (0, -1)]
         if self.hard_mode and random.random() < 0.5:
-            temp = self.action_mapping[1]
-            self.action_mapping[1] = self.action_mapping[3]
-            self.action_mapping[3] = temp
+            # flip two control directions for an extra challenge
+            act_map[1], act_map[3] = act_map[3], act_map[1]
+        self.action_mapping = act_map
         self.current_k = 0
         return self.soft_reset()
 
@@ -181,13 +182,14 @@ def step(self, action):
         else:
             reward = 0.0
             soft_reset = False
-
         self.x = next_x
         self.y = next_y
 
         if soft_reset:
-            self.current_k += 1
             next_state, info = self.soft_reset()
+            success = on == "G"
+            info[f"{AMAGO_ENV_LOG_PREFIX}Episode {self.current_k} Success"] = success
+            self.current_k += 1
         else:
             next_state, info = self.make_obs(False), {}
 
diff --git a/amago/envs/builtin/tmaze.py b/amago/envs/builtin/tmaze.py
index e613b90..f255d28 100644
--- a/amago/envs/builtin/tmaze.py
+++ b/amago/envs/builtin/tmaze.py
@@ -58,9 +58,9 @@ def __init__(
         )
         self.bias_x, self.bias_y = 1, 2
         self.tmaze_map[self.bias_y, self.bias_x : -self.bias_x] = True  # corridor
-        self.tmaze_map[[self.bias_y - 1, self.bias_y + 1], -self.bias_x - 1] = (
-            True  # goal candidates
-        )
+        self.tmaze_map[
+            [self.bias_y - 1, self.bias_y + 1], -self.bias_x - 1
+        ] = True  # goal candidates
 
         obs_dim = 2 if self.ambiguous_position else 3
         if self.expose_goal:  # test Markov policies
diff --git a/amago/envs/env_utils.py b/amago/envs/env_utils.py
index cf185e1..be0dd61 100644
--- a/amago/envs/env_utils.py
+++ b/amago/envs/env_utils.py
@@ -6,7 +6,7 @@
 from uuid import uuid4
 from dataclasses import dataclass
 from collections import defaultdict
-from typing import Optional, Type, Callable, Iterable
+from typing import Optional, Type, Callable, Iterable, Any
 
 import gymnasium as gym
 import numpy as np
@@ -327,7 +327,36 @@ def add_score(self, env_name, score):
 SuccessHistory = ReturnHistory
 
 
+class SpecialMetricHistory:
+    log_prefix = "AMAGO_LOG_METRIC"
+
+    def __init__(self, env_name):
+        self.data = {}
+
+    def add_score(self, env_name: str, key: str, value: Any):
+        if key.startswith(self.log_prefix):
+            key = key[len(self.log_prefix) :].strip()
+        if env_name not in self.data:
+            self.data[env_name] = {}
+        if key not in self.data[env_name]:
+            self.data[env_name][key] = [value]
+        else:
+            self.data[env_name][key].append(value)
+
+
+AMAGO_ENV_LOG_PREFIX = SpecialMetricHistory.log_prefix
+
+
 class SequenceWrapper(gym.Wrapper):
+    """
+    A wrapper that handles automatic resets, saving trajectory files to disk,
+    and rollout metrics. Automatically logs total return in all envs.
+    When using the goal-conditioned / relabeling system, this will
+    log meaningful total success rates. We also log any metric from the
+    gym env's `info` dict that begins with "AMAGO_LOG_METRIC"
+    (`amago.envs.env_utils.AMAGO_ENV_LOG_PREFIX`).
+    """
+
     def __init__(
         self,
         env,
@@ -388,6 +417,7 @@ def reset_stats(self):
         # stores all of the success/return histories
         self.return_history = ReturnHistory(self.env_name)
         self.success_history = SuccessHistory(self.env_name)
+        self.special_history = SpecialMetricHistory(self.env_name)
 
     def reset(self, seed=None) -> Timestep:
         timestep, info = self.env.reset(seed=seed)
@@ -407,6 +437,10 @@ def step(self, action):
         self.total_return += reward
         self.active_traj.add_timestep(timestep)
         self.since_last_save += 1
+        for info_key, info_val in info.items():
+            if info_key.startswith(self.special_history.log_prefix):
+                self.special_history.add_score(self.env.env_name, info_key, info_val)
+
         if timestep.terminal:
             self.return_history.add_score(self.env.env_name, self.total_return)
             success = (
@@ -415,6 +449,7 @@ def step(self, action):
                 else info["success"]
             )
             self.success_history.add_score(self.env.env_name, success)
+
         save = (
             self.save_every is not None and self.since_last_save > self.save_this_time
         )
diff --git a/amago/learning.py b/amago/learning.py
index ac67f80..70ef456 100644
--- a/amago/learning.py
+++ b/amago/learning.py
@@ -23,6 +23,7 @@
 from amago.envs.env_utils import (
     ReturnHistory,
     SuccessHistory,
+    SpecialMetricHistory,
     ExplorationWrapper,
     EpsilonGreedy,
     SequenceWrapper,
@@ -73,7 +74,7 @@ class Experiment:
     start_learning_at_epoch: int = 0
     start_collecting_at_epoch: int = 0
     train_timesteps_per_epoch: int = 1000
-    train_grad_updates_per_epoch: int = 1000
+    train_batches_per_epoch: int = 1000
     val_interval: Optional[int] = 10
     val_timesteps_per_epoch: int = 10_000
     log_interval: int = 250
@@ -300,7 +301,9 @@ def init_dsets(self):
             dset_root=self.dset_root,
             dset_name=self.dset_name,
             dset_split="train",
-            items_per_epoch=self.train_grad_updates_per_epoch * self.batch_size,
+            items_per_epoch=self.train_batches_per_epoch
+            * self.batch_size
+            * self.accelerator.num_processes,
             max_seq_len=self.max_seq_len,
         )
 
@@ -463,16 +466,24 @@ def get_t(_dones=None):
 
         return_history = utils.call_async_env(envs, "return_history")
         success_history = utils.call_async_env(envs, "success_history")
+        special_history = utils.call_async_env(envs, "special_history")
         return (
             (obs_seqs, goal_seqs, rl2_seqs),
             hidden_state,
             return_history,
             success_history,
+            special_history,
         )
 
     def collect_new_training_data(self):
         if self.train_timesteps_per_epoch > 0:
-            self.train_buffers, self.hidden_state, returns, successes = self.interact(
+            (
+                self.train_buffers,
+                self.hidden_state,
+                returns,
+                successes,
+                specials,
+            ) = self.interact(
                 self.train_envs,
                 self.train_timesteps_per_epoch,
                 buffers=self.train_buffers,
@@ -480,11 +491,13 @@ def collect_new_training_data(self):
 
     def evaluate_val(self):
         if self.val_timesteps_per_epoch > 0:
-            *_, returns, successes = self.interact(
+            *_, returns, successes, specials = self.interact(
                 self.val_envs,
                 self.val_timesteps_per_epoch,
             )
-            logs_per_process = self.policy_metrics(returns, successes)
+            logs_per_process = self.policy_metrics(
+                returns, successes, specials=specials
+            )
             cur_return = logs_per_process["Average Total Return (Across All Env Names)"]
             if self.verbose:
                 self.accelerator.print(f"Average Return : {cur_return}")
@@ -499,12 +512,12 @@ def evaluate_test(
         )
         Par = gym.vector.AsyncVectorEnv if self.async_envs else DummyAsyncVectorEnv
         test_envs = Par([make for _ in range(self.parallel_actors)])
-        *_, returns, successes = self.interact(
+        *_, returns, successes, specials = self.interact(
             test_envs,
             timesteps,
             render=render,
         )
-        logs = self.policy_metrics(returns, successes)
+        logs = self.policy_metrics(returns, successes, specials)
         self.log(logs, key="test")
         test_envs.close()
         return logs
@@ -544,35 +557,46 @@ def make_figures(self, loss_info) -> dict[str, wandb.Image]:
         """
         return {}
 
-    def policy_metrics(self, returns: ReturnHistory, successes: SuccessHistory):
-        return_by_env_name = {}
-        success_by_env_name = {}
-        for ret, suc in zip(returns, successes):
+    def policy_metrics(
+        self,
+        returns: ReturnHistory,
+        successes: SuccessHistory,
+        specials: SpecialMetricHistory,
+    ) -> dict:
+        returns_by_env_name = defaultdict(list)
+        success_by_env_name = defaultdict(list)
+        specials_by_env_name = defaultdict(lambda: defaultdict(list))
+
+        for ret, suc, spe in zip(returns, successes, specials):
             for env_name, scores in ret.data.items():
-                if env_name in return_by_env_name:
-                    return_by_env_name[env_name] += scores
-                else:
-                    return_by_env_name[env_name] = scores
+                returns_by_env_name[env_name].extend(scores)
             for env_name, scores in suc.data.items():
-                if env_name in success_by_env_name:
-                    success_by_env_name[env_name] += scores
-                else:
-                    success_by_env_name[env_name] = scores
+                success_by_env_name[env_name].extend(scores)
+            for env_name, specials_dict in spe.data.items():
+                for special_key, special_val in specials_dict.items():
+                    specials_by_env_name[env_name][special_key].extend(special_val)
 
         avg_ret_per_env = {
             f"Average Total Return in {name}": np.array(scores).mean()
-            for name, scores in return_by_env_name.items()
+            for name, scores in returns_by_env_name.items()
         }
         avg_suc_per_env = {
             f"Average Success Rate in {name}": np.array(scores).mean()
             for name, scores in success_by_env_name.items()
         }
+        avg_special_per_env = {
+            f"Average {special_key} in {name}": np.array(special_vals).mean()
+            for name, specials_dict in specials_by_env_name.items()
+            for special_key, special_vals in specials_dict.items()
+        }
         avg_return_overall = {
             "Average Total Return (Across All Env Names)": np.array(
                 list(avg_ret_per_env.values())
             ).mean()
         }
-        return avg_ret_per_env | avg_suc_per_env | avg_return_overall
+        return (
+            avg_ret_per_env | avg_suc_per_env | avg_return_overall | avg_special_per_env
+        )
 
     def compute_loss(self, batch: Batch, log_step: bool):
         critic_loss, actor_loss = self.policy_aclr(batch, log_step=log_step)
@@ -637,7 +661,7 @@ def make_pbar(loader, epoch_num):
                 return tqdm(
                     enumerate(loader),
                     desc=f"{self.run_name} Epoch {epoch_num} Train",
-                    total=self.train_grad_updates_per_epoch,
+                    total=self.train_batches_per_epoch,
                     colour="green",
                 )
             else:
@@ -667,7 +691,7 @@ def make_pbar(loader, epoch_num):
                 continue
             self.policy_aclr.train()
             for train_step, batch in make_pbar(self.train_dloader, epoch):
-                total_step = (epoch * self.train_grad_updates_per_epoch) + train_step
+                total_step = (epoch * self.train_batches_per_epoch) + train_step
                 log_step = total_step % self.log_interval == 0
                 loss_dict = self.train_step(batch, log_step=log_step)
                 if log_step:
diff --git a/examples/00_kshot_frozen_lake.py b/examples/00_kshot_frozen_lake.py
index 9e7e072..a699852 100644
--- a/examples/00_kshot_frozen_lake.py
+++ b/examples/00_kshot_frozen_lake.py
@@ -14,7 +14,6 @@ def add_cli(parser):
     )
     parser.add_argument("--run_name", type=str, required=True)
     parser.add_argument("--buffer_dir", type=str, required=True)
-    parser.add_argument("--gpu", type=int, required=True)
     parser.add_argument("--log", action="store_true")
     parser.add_argument("--trials", type=int, default=3)
     parser.add_argument("--lake_size", type=int, default=5)
@@ -87,10 +86,10 @@ def add_cli(parser):
             make_val_env=make_env,
             max_seq_len=args.max_rollout_length,
             traj_save_len=args.max_rollout_length,
+            agent_type=amago.agent.Agent,
             dset_max_size=10_000,
             run_name=run_name,
             dset_name=run_name,
-            gpu=args.gpu,
             dset_root=args.buffer_dir,
             dloader_workers=10,
             log_to_wandb=args.log,
@@ -98,7 +97,7 @@ def add_cli(parser):
             epochs=500 if not args.hard_mode else 900,
             parallel_actors=24,
             train_timesteps_per_epoch=350,
-            train_grad_updates_per_epoch=700,
+            train_batches_per_epoch=700,
             val_interval=20,
             val_timesteps_per_epoch=args.max_rollout_length * 2,
             ckpt_interval=50,