diff --git a/Playgrounds/nanosocrates-train-experiment-2.py b/Playgrounds/nanosocrates-train-experiment-2.py
index 091f5ed..5f7ec67 100644
--- a/Playgrounds/nanosocrates-train-experiment-2.py
+++ b/Playgrounds/nanosocrates-train-experiment-2.py
@@ -59,7 +59,8 @@ CHECKPOINT_STEPS = VALIDATION_STEPS * 1000
 PATIENCE = 4
 CURRENT_EPOCH = -1 if not LAST_EPOCH_PATH.is_file() else int(LAST_EPOCH_PATH.read_text())
 VERBOSE = True
-LEARNING_RATE = 1.5
+LEARNING_RATE = 0.05
+LABEL_SMOOTHING = 0.01
 
 SOS_TOKEN = TOKENANO.encode("<SOS>")[0]
 
@@ -103,9 +104,9 @@ _, ENCODER_ONLY, DECODER_ONLY = TUtils.decompose_nano_socrates(
 
 
 # Training constants
-nano_cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)
-encoder_ce = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)
-decoder_ce = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)
+nano_cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN, label_smoothing=LABEL_SMOOTHING)
+encoder_ce = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN, label_smoothing=LABEL_SMOOTHING)
+decoder_ce = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN, label_smoothing=LABEL_SMOOTHING)
 nano_optim = torch.optim.AdamW(NANOSOCRATES.parameters(), LEARNING_RATE)
 encoder_only_optim = torch.optim.AdamW(ENCODER_ONLY.parameters(), LEARNING_RATE)
 decoder_only_optim = torch.optim.AdamW(DECODER_ONLY.parameters(), LEARNING_RATE)
@@ -352,16 +353,22 @@ while current_epoch < MAX_EPOCHS:
                 if VERBOSE:
                     print("txt average is higher than lowest")
                 counter += 1
+            else:
+                average_loss_validation["txt"] = txt_avg_loss
 
             if enc_avg_loss > average_loss_validation["encoder_only"]:
                 if VERBOSE:
                     print("masking average is higher than lowest")
                 counter += 1
+            else:
+                average_loss_validation["encoder_only"] = enc_avg_loss
 
             if dec_avg_loss > average_loss_validation["decoder_only"]:
                 if VERBOSE:
                     print("decoding only average is higher than lowest")
                 counter += 1
+            else:
+                average_loss_validation["decoder_only"] = dec_avg_loss
 
             if counter > 1:
                 patience += 1