diff --git a/crates/ml/src/cuda_pipeline/mod.rs b/crates/ml/src/cuda_pipeline/mod.rs
index 981ef4b26..1b28bbb26 100644
--- a/crates/ml/src/cuda_pipeline/mod.rs
+++ b/crates/ml/src/cuda_pipeline/mod.rs
@@ -286,7 +286,7 @@ impl DqnGpuData {
         }
         if ofi.len() != num_bars {
             return Err(MLError::ModelError(format!(
-                "OFI/market data length mismatch: {} OFI rows vs {} bars — fxcache is corrupt",
+                "OFI/market data length mismatch: {} OFI rows vs {} bars — upstream truncation desync (check data_loading.rs max_bars paths)",
                 ofi.len(), num_bars,
             )));
         }
diff --git a/crates/ml/src/trainers/dqn/data_loading.rs b/crates/ml/src/trainers/dqn/data_loading.rs
index 9207c6575..ced3da860 100644
--- a/crates/ml/src/trainers/dqn/data_loading.rs
+++ b/crates/ml/src/trainers/dqn/data_loading.rs
@@ -157,8 +157,8 @@ impl DQNTrainer {
             }
             let ofi_nonzero = cached.ofi.iter().filter(|r| r.iter().any(|&v| v != 0.0)).count();
             tracing::info!("OFI loaded from fxcache: {} bars, {} non-zero rows", cached.ofi.len(), ofi_nonzero);
-            self.ofi_features = Some(Arc::from(cached.ofi));
 
+            let mut ofi_data = cached.ofi;
             let mut all_data: Vec<(FeatureVector, Vec<f64>)> = cached.features
                 .into_iter()
                 .zip(cached.targets.into_iter())
@@ -167,11 +167,18 @@ impl DQNTrainer {
                 })
                 .collect();
 
-            // Truncate to max_bars if configured (0 = unlimited)
+            // Truncate to max_bars if configured (0 = unlimited).
+            // OFI must be truncated in lockstep — downstream GPU upload asserts
+            // ofi.len() == num_bars and the desync previously surfaced as a
+            // misleading "fxcache is corrupt" error.
             if self.hyperparams.max_bars > 0 && all_data.len() > self.hyperparams.max_bars {
-                tracing::info!("max_bars={}: truncating {} → {} bars", self.hyperparams.max_bars, all_data.len(), self.hyperparams.max_bars);
+                tracing::info!("max_bars={}: truncating {} → {} bars (incl. OFI)", self.hyperparams.max_bars, all_data.len(), self.hyperparams.max_bars);
                 all_data.truncate(self.hyperparams.max_bars);
+                ofi_data.truncate(self.hyperparams.max_bars);
             }
+            debug_assert_eq!(ofi_data.len(), all_data.len(),
+                "OFI and market-data lengths must match after truncation");
+            self.ofi_features = Some(Arc::from(ofi_data));
 
             let split = (all_data.len() * 80) / 100;
             let train = all_data[..split].to_vec();
@@ -497,11 +504,22 @@ impl DQNTrainer {
             feature_dim
         );
 
-        // Truncate to max_bars if configured (0 = unlimited)
+        // Truncate to max_bars if configured (0 = unlimited).
+        // OFI (if present from the DBN path above) must be truncated in lockstep —
+        // downstream GPU upload asserts ofi.len() == num_bars.
         if self.hyperparams.max_bars > 0 && training_data.len() > self.hyperparams.max_bars {
-            tracing::info!("max_bars={}: truncating {} → {} bars", self.hyperparams.max_bars, training_data.len(), self.hyperparams.max_bars);
+            tracing::info!("max_bars={}: truncating {} → {} bars (incl. OFI)", self.hyperparams.max_bars, training_data.len(), self.hyperparams.max_bars);
             training_data.truncate(self.hyperparams.max_bars);
+            if let Some(ofi_arc) = self.ofi_features.take() {
+                let mut ofi_vec: Vec<[f64; 20]> = ofi_arc.iter().copied().collect();
+                ofi_vec.truncate(self.hyperparams.max_bars);
+                self.ofi_features = Some(Arc::from(ofi_vec));
+            }
         }
+        debug_assert!(
+            self.ofi_features.as_ref().map_or(true, |o| o.len() == training_data.len()),
+            "OFI and training-data lengths must match after truncation",
+        );
 
         // Split training data 80/20 for train/validation
         let split_idx = (training_data.len() * 80) / 100;