diff --git a/data/data_loader.py b/data/data_loader.py
index 1ab9b67..12706e8 100644
--- a/data/data_loader.py
+++ b/data/data_loader.py
@@ -117,8 +117,19 @@ class DataLoaderMultiAspect():
                 buckets[bucket_key] = []
             buckets[bucket_key].append(image_caption_pair)
 
-        # handle runts by randomly duplicating items
+        # handled named batch runts by demoting them to the DEFAULT_BATCH_ID
+        for key, bucket in [(k, b) for k, b in buckets.items() if k[0] != DEFAULT_BATCH_ID]:
+            runt_count = len(bucket) % batch_size
+            if runt_count == 0:
+                continue
+            runts = bucket[-runt_count:]
+            del bucket[-runt_count:]
+            matching_default_bucket_key = [DEFAULT_BATCH_ID, key[1], key[2]]
+            buckets[matching_default_bucket_key].extend(runts)
+
+        # handle remaining runts by randomly duplicating items
         for bucket in buckets:
+            assert bucket[0] == DEFAULT_BATCH_ID, "there should be no more runts in named batches"
             truncate_count = len(buckets[bucket]) % batch_size
             if truncate_count > 0:
                 runt_bucket = buckets[bucket][-truncate_count:]
@@ -132,9 +143,21 @@ class DataLoaderMultiAspect():
                 buckets[bucket] = buckets[bucket][:current_bucket_size - truncate_count]
                 buckets[bucket].extend(runt_bucket)
 
+        items_by_batch_id = collapse_buckets_by_batch_id(buckets)
+        # at this point items have a partially deterministic order
+        # (in particular: rarer aspect ratios are more likely to cluster at the end due to stochastic sampling)
+        # so we shuffle them to mitigate this, using chunked_shuffle to keep batches with the same aspect ratio together
+        items_by_batch_id = {k: chunked_shuffle(v, chunk_size=batch_size, randomizer=randomizer)
+                             for k,v in items_by_batch_id.items()}
+        # paranoia: verify that this hasn't fucked up the aspect ratio batching
+        for items in items_by_batch_id.values():
+            batches = chunk(items, chunk_size=batch_size)
+            for batch in batches:
+                target_wh = batch[0].target_wh
+                assert all(target_wh == i.target_wh for i in batch[1:]), "mixed aspect ratios in a batch - this shouldn't happen"
+
         # handle batch_id
         # unlabelled data (no batch_id) is in batches labelled DEFAULT_BATCH_ID.
-        items_by_batch_id = collapse_buckets_by_batch_id(buckets)
         items = flatten_buckets_preserving_named_batch_adjacency(items_by_batch_id,
                                                                    batch_size=batch_size,
                                                                    grad_accum=grad_accum)