diff --git a/BLcourse2.3/01_one_dim.py b/BLcourse2.3/01_one_dim.py
index 9043f2aaa0378e9db4bfec4eca37f61e3d5d5dc7..66bafc5b89f8a4b4f30736d69ac18a0f153a31a6 100644
--- a/BLcourse2.3/01_one_dim.py
+++ b/BLcourse2.3/01_one_dim.py
@@ -354,11 +354,14 @@ for ii in range(n_iter):
 
 # Plot hyper params and loss (negative log marginal likelihood) convergence
 ncols = len(history)
-fig, axs = plt.subplots(ncols=ncols, nrows=1, figsize=(ncols * 5, 5))
-for ax, (p_name, p_lst) in zip(axs, history.items()):
-    ax.plot(p_lst)
-    ax.set_title(p_name)
-    ax.set_xlabel("iterations")
+fig, axs = plt.subplots(
+    ncols=ncols, nrows=1, figsize=(ncols * 3, 3), layout="compressed"
+)
+with torch.no_grad():
+    for ax, (p_name, p_lst) in zip(axs, history.items()):
+        ax.plot(p_lst)
+        ax.set_title(p_name)
+        ax.set_xlabel("iterations")
 
 # Values of optimized hyper params
 pprint(extract_model_params(model))
@@ -387,7 +390,7 @@ with torch.no_grad():
     post_pred_f = model(X_pred)
     post_pred_y = likelihood(model(X_pred))
 
-    fig, axs = plt.subplots(ncols=2, figsize=(12, 5), sharex=True, sharey=True)
+    fig, axs = plt.subplots(ncols=2, figsize=(14, 5), sharex=True, sharey=True)
     fig_sigmas, ax_sigmas = plt.subplots()
     for ii, (ax, post_pred, name, title) in enumerate(
         zip(