diff --git a/exps/LFNA/lfna.py b/exps/LFNA/lfna.py
index 4a1ecfa..bba6025 100644
--- a/exps/LFNA/lfna.py
+++ b/exps/LFNA/lfna.py
@@ -3,7 +3,7 @@
 #####################################################
 # python exps/LFNA/lfna.py --env_version v1 --workers 0
 # python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.001
-# python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.002
+# python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.002 --meta_batch 128
 #####################################################
 import sys, time, copy, torch, random, argparse
 from tqdm import tqdm
@@ -164,7 +164,7 @@ def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
             timestamp = meta_model.meta_timestamps[rand_index]
             meta_embed = meta_model.super_meta_embed[rand_index]
 
-            timestamps, [container], time_embeds = meta_model(
+            _, [container], time_embed = meta_model(
                 torch.unsqueeze(timestamp, dim=0), None, True
             )
             _, (inputs, targets) = xenv(timestamp.item())
@@ -173,30 +173,12 @@ def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
             predictions = base_model.forward_with_container(inputs, container)
             total_meta_v1_losses.append(criterion(predictions, targets))
             # the matching loss
-            match_loss = criterion(torch.squeeze(time_embeds, dim=0), meta_embed)
+            match_loss = criterion(torch.squeeze(time_embed, dim=0), meta_embed)
             total_match_losses.append(match_loss)
             # generate models via memory
-            rand_index = random.randint(0, meta_model.meta_length - xenv.seq_length - 1)
-            _, [seq_containers], _ = meta_model(
-                None,
-                torch.unsqueeze(
-                    meta_model.super_meta_embed[
-                        rand_index : rand_index + xenv.seq_length
-                    ],
-                    dim=0,
-                ),
-                False,
-            )
-            timestamps = meta_model.meta_timestamps[
-                rand_index : rand_index + xenv.seq_length
-            ]
-            _, (seq_inputs, seq_targets) = xenv.seq_call(timestamps)
-            seq_inputs, seq_targets = seq_inputs.to(device), seq_targets.to(device)
-            for container, inputs, targets in zip(
-                seq_containers, seq_inputs, seq_targets
-            ):
-                predictions = base_model.forward_with_container(inputs, container)
-                total_meta_v2_losses.append(criterion(predictions, targets))
+            _, [container], _ = meta_model(None, meta_embed.view(1, 1, -1), True)
+            predictions = base_model.forward_with_container(inputs, container)
+            total_meta_v2_losses.append(criterion(predictions, targets))
         with torch.no_grad():
             meta_std = torch.stack(total_meta_v1_losses).std().item()
         meta_v1_loss = torch.stack(total_meta_v1_losses).mean()
@@ -564,8 +546,9 @@ if __name__ == "__main__":
     if args.rand_seed is None or args.rand_seed < 0:
         args.rand_seed = random.randint(1, 100000)
     assert args.save_dir is not None, "The save dir argument can not be None"
-    args.save_dir = "{:}-d{:}_{:}_{:}-s{:}-lr{:}-wd{:}-e{:}-env{:}".format(
+    args.save_dir = "{:}-bs{:}-d{:}_{:}_{:}-s{:}-lr{:}-wd{:}-e{:}-env{:}".format(
         args.save_dir,
+        args.meta_batch,
         args.hidden_dim,
         args.layer_dim,
         args.time_dim,