diff --git a/training/amygdala_training/train_with_library.py b/training/amygdala_training/train_with_library.py
index 224eb3d..23633eb 100644
--- a/training/amygdala_training/train_with_library.py
+++ b/training/amygdala_training/train_with_library.py
@@ -84,16 +84,31 @@ def _samples_for_concept(
     return samples
 
 
+def _fp32_wrap(inner):
+    """Wrap an aggregator so activations are cast to fp32 first.
+
+    torch.svd / torch.linalg.svd don't support bf16 on either CUDA or CPU,
+    and Qwen3.5 runs in bf16. Cast before the aggregator sees the tensors.
+    """
+
+    def wrapped(pos_acts: torch.Tensor, neg_acts: torch.Tensor) -> torch.Tensor:
+        return inner(pos_acts.to(torch.float32), neg_acts.to(torch.float32))
+
+    return wrapped
+
+
 def _aggregator_from_name(name: str):
     if name == "mean":
-        return mean_aggregator()
+        return _fp32_wrap(mean_aggregator())
     if name == "pca":
-        return pca_aggregator()
+        return _fp32_wrap(pca_aggregator())
     if name == "logistic":
-        return logistic_aggregator()
+        return _fp32_wrap(logistic_aggregator())
     if name == "logistic_l1":
-        return logistic_aggregator(
-            sklearn_kwargs={"penalty": "l1", "solver": "liblinear", "C": 0.1}
+        return _fp32_wrap(
+            logistic_aggregator(
+                sklearn_kwargs={"penalty": "l1", "solver": "liblinear", "C": 0.1}
+            )
         )
     raise ValueError(f"unknown aggregator: {name}")