diff --git a/egomimic/eval/latent_dataset.py b/egomimic/eval/latent_dataset.py
index 161bbd09e..3d8edec8d 100644
--- a/egomimic/eval/latent_dataset.py
+++ b/egomimic/eval/latent_dataset.py
@@ -61,6 +61,11 @@
         "_shuffle_random",
         "_shuffle_pairs",
         "_shuffle_custom",
+        "use_tokenizer",
+        "model_name",
+        "sampling_mode",
+        "annotation_key",
+        "default_prompt",
     }
 )
 
@@ -86,18 +91,38 @@ def build_dataset(
     swallowing) — typos in yaml fail loudly.
     """
     if mode == "random":
-        lam = (
-            "lambda row: row['task'] == "
-            f"{task!r} and row['robot_name'] == {embodiment!r}"
-        )
-        filters = DatasetFilter(filter_lambdas=[lam])
-        logger.info("[build_dataset] %s | random mode | filter=%s", embodiment, lam)
-        return MultiDataset._from_resolver(
+        if filters is not None:
+            logger.info(
+                "[build_dataset] %s | random mode | using passed filters", embodiment
+            )
+        else:
+            lam = (
+                "lambda row: row['task'] == "
+                f"{task!r} and row['robot_name'] == {embodiment!r}"
+            )
+            filters = DatasetFilter(filter_lambdas=[lam])
+            logger.info("[build_dataset] %s | random mode | filter=%s", embodiment, lam)
+        base = MultiDataset._from_resolver(
             resolver,
             filters=filters,
             mode="total",
             valid_ratio=valid_ratio,
         )
+        if stride is not None and stride > 0:
+            logger.info(
+                "[build_dataset] %s | random+stride=%d wrapping",
+                embodiment,
+                stride,
+            )
+            return EvenStrideDataset(base, stride=stride)
+        if frames_per_episode is not None:
+            logger.info(
+                "[build_dataset] %s | random+frames_per_episode=%d wrapping",
+                embodiment,
+                frames_per_episode,
+            )
+            return EvenStrideDataset(base, frames_per_episode=frames_per_episode)
+        return base
 
     if mode in ("pairs", "custom"):
         if not hashes: