diff --git a/bin/sample.py b/bin/sample.py
index d836dec..f417e10 100644
--- a/bin/sample.py
+++ b/bin/sample.py
@@ -93,9 +93,13 @@ def plot_distribution_overlap(
     fname: str = "",
     ax=None,
     show_legend: bool = True,
+    **kwargs,
 ):
     """
     Plot the distribution overlap between the training and sampled values
+    Additional arguments are given to ax.hist; for example, can specify
+    histtype='step', cumulative=True
+    to get a CDF plot
     """
     # Plot the distribution overlap
     logging.info(f"Plotting distribution overlap for {ft_name}")
@@ -106,18 +110,18 @@ def plot_distribution_overlap(
         bins=50,
         density=True,
         label="Training",
-        color="tab:blue",
+        # color="tab:blue",
         alpha=0.6,
-        edgecolor="black",
+        **kwargs,
     )
     ax.hist(
         sampled_values,
         bins=bins,
         density=True,
         label="Sampled",
-        color="tab:orange",
+        # color="tab:orange",
         alpha=0.6,
-        edgecolor="black",
+        **kwargs,
     )
     ax.set(title=f"Sampled distribution - {ft_name}")
     if show_legend:
@@ -267,9 +271,12 @@ def main() -> None:
             model,
             train_dset,
             n=10,
-            sweep_lengths=(50, train_dset.dset.pad),
+            # sweep_lengths=(50, train_dset.dset.pad),
+            sweep_lengths=(50, 52),  # Dummy values
             batch_size=args.batchsize,
         )
+    else:
+        raise ValueError(f"Unrecognized length mode: {args.lengths}")
     final_sampled = [s[-1] for s in sampled]
     sampled_dfs = [
         pd.DataFrame(s, columns=train_dset.feature_names["angles"])
@@ -316,6 +323,9 @@ def main() -> None:
     multi_fig, multi_axes = plt.subplots(
         dpi=300, nrows=2, ncols=3, figsize=(14, 6), sharex=True
     )
+    step_multi_fig, step_multi_axes = plt.subplots(
+        dpi=300, nrows=2, ncols=3, figsize=(14, 6), sharex=True
+    )
     final_sampled_stacked = np.vstack(final_sampled)
     for i, ft_name in enumerate(train_dset.feature_names["angles"]):
         orig_values = train_values_stacked[:, i]
@@ -323,6 +333,14 @@ def main() -> None:
         plot_distribution_overlap(
             orig_values, samp_values, ft_name, fname=plotdir / f"dist_{ft_name}.pdf"
         )
+        plot_distribution_overlap(
+            orig_values,
+            samp_values,
+            ft_name,
+            histtype="step",
+            cumulative=True,
+            fname=plotdir / f"cdf_{ft_name}.pdf",
+        )
         plot_distribution_overlap(
             orig_values,
             samp_values,
@@ -330,7 +348,17 @@ def main() -> None:
             ax=multi_axes.flatten()[i],
             show_legend=i == 0,
         )
+        plot_distribution_overlap(
+            orig_values,
+            samp_values,
+            ft_name,
+            cumulative=True,
+            histtype="step",
+            ax=step_multi_axes.flatten()[i],
+            show_legend=i == 0,
+        )
     multi_fig.savefig(plotdir / "dist_combined.pdf", bbox_inches="tight")
+    step_multi_fig.savefig(plotdir / "cdf_combined.pdf", bbox_inches="tight")
 
     # Generate ramachandran plot for sampled angles
     plotting.plot_joint_kde(