diff --git a/crates/ml/src/trainers/dqn/smoke_tests/training_stability.rs b/crates/ml/src/trainers/dqn/smoke_tests/training_stability.rs
index c220bfb4f..fee6945b2 100644
--- a/crates/ml/src/trainers/dqn/smoke_tests/training_stability.rs
+++ b/crates/ml/src/trainers/dqn/smoke_tests/training_stability.rs
@@ -547,7 +547,14 @@ fn test_fxcache_zero_copy_training() -> anyhow::Result<()> {
     eprintln!("[FXCACHE] GPU upload complete");
 
     // 5. Train each fold using the production zero-copy path
-    let mut fold_losses = Vec::new();
+    //
+    // Previously this loop swallowed every training error into f64::NAN and asserted
+    // only !fold_losses.is_empty() after unconditionally pushing one entry at the
+    // top — a tautology that could never fail, masking CUDA errors, NaN explosions,
+    // and any regression of the zero-copy path. Errors now propagate via `?` so
+    // the test actually validates the path's behaviour, not just that it runs.
+    let mut fold_losses: Vec<f64> = Vec::new();
+    let mut fold_epochs: Vec<u32> = Vec::new();
     for range in &fold_ranges {
         let train_feat = &fxcache_data.features[range.train_start..range.train_end];
         let val_feat = &fxcache_data.features[range.val_start..range.val_end];
@@ -563,27 +570,41 @@ fn test_fxcache_zero_copy_training() -> anyhow::Result<()> {
         rt.block_on(trainer.reset_for_fold())?;
 
         let fold = range.fold;
-        let result = rt.block_on(trainer.train_fold_from_slices(
+        let metrics = rt.block_on(trainer.train_fold_from_slices(
             train_feat, train_targets,
             |_epoch, _bytes, _is_best| Ok(String::new()),
-        ));
-
-        match result {
-            Ok(metrics) => {
-                eprintln!("[FXCACHE] Fold {} complete: loss={:.4}, epochs={}", fold, metrics.loss, metrics.epochs_trained);
-                fold_losses.push(metrics.loss);
-            }
-            Err(e) => {
-                // NaN at early steps can happen with raw features + small smoketest network.
-                // The important thing is the code PATH works (no hang, no CUDA error).
-                eprintln!("[FXCACHE] Fold {} error (non-fatal for path validation): {:#}", fold, e);
-                fold_losses.push(f64::NAN);
-            }
-        }
+        ))?;
+        eprintln!(
+            "[FXCACHE] Fold {} complete: loss={:.4}, epochs={}",
+            fold, metrics.loss, metrics.epochs_trained,
+        );
+        fold_losses.push(metrics.loss);
+        fold_epochs.push(metrics.epochs_trained);
     }
 
-    // 6. Verify training ran (code path validation, not training quality)
-    assert!(!fold_losses.is_empty(), "No folds completed");
+    // 6. Real assertions on the zero-copy path.
+    //    NOTE: directly asserting "no htod/dtoh copy happened" would require
+    //    instrumenting the fused-training GPU data path with a copy-counter and
+    //    exposing it through TrainingMetrics, which is out-of-scope here. What we
+    //    can assert end-to-end is that the path produced a *valid training run*:
+    //    any NaN/Inf/negative loss or zero-epoch fold indicates that the zero-copy
+    //    wiring regressed (buffer not populated, kernel skipped, etc.).
+    assert!(!fold_losses.is_empty(), "fxcache zero-copy path produced zero folds");
+    assert!(
+        fold_losses.iter().all(|l| l.is_finite()),
+        "fxcache zero-copy training produced NaN/Inf loss: {:?}",
+        fold_losses,
+    );
+    assert!(
+        fold_losses.iter().all(|l| *l >= 0.0),
+        "fxcache zero-copy training produced negative loss (sign bug?): {:?}",
+        fold_losses,
+    );
+    assert!(
+        fold_epochs.iter().all(|&e| e >= 1),
+        "fxcache zero-copy training completed 0 epochs in some fold: {:?}",
+        fold_epochs,
+    );
 
     eprintln!("[FXCACHE] All {} folds passed. Losses: {:?}", fold_losses.len(), fold_losses);
     Ok(())