diff --git a/crates/ml/src/trainers/dqn/trainer.rs b/crates/ml/src/trainers/dqn/trainer.rs
index 9693a61dd..d4de0afd2 100644
--- a/crates/ml/src/trainers/dqn/trainer.rs
+++ b/crates/ml/src/trainers/dqn/trainer.rs
@@ -1264,7 +1264,16 @@ impl DQNTrainer {
     async fn get_q_values(&self, state: &TradingState) -> Result<Vec<f64>> {
         let agent = self.agent.read().await;
         let state_vec = state.to_vector();
-        let state_tensor = Tensor::new(&*state_vec, &self.device)?.unsqueeze(0)?; // Add batch dimension
+        let raw_dim = state_vec.len();
+        let aligned = crate::dqn::mixed_precision::align_dim_for_tensor_cores(raw_dim, &self.device);
+        let padded: Vec<f32> = if aligned > raw_dim {
+            let mut v = state_vec.to_vec();
+            v.resize(aligned, 0.0);
+            v
+        } else {
+            state_vec.to_vec()
+        };
+        let state_tensor = Tensor::new(&*padded, &self.device)?.unsqueeze(0)?; // Add batch dimension
 
         let q_values_tensor = agent.forward(&state_tensor)?;
         let q_values_vec = q_values_tensor.squeeze(0)?.to_vec1::<f32>()?;
@@ -4221,11 +4230,20 @@ impl DQNTrainer {
         // Use internal conversion method (returns TradingState)
         let trading_state = self.feature_vector_to_state(feature_vec, Some(close))?;
 
-        // Convert TradingState to flat vector
+        // Convert TradingState to flat vector, pad for tensor core alignment
         let state_vec = trading_state.to_vector();
+        let raw_dim = state_vec.len();
+        let aligned = crate::dqn::mixed_precision::align_dim_for_tensor_cores(raw_dim, &self.device);
+        let padded: Vec<f32> = if aligned > raw_dim {
+            let mut v = state_vec.to_vec();
+            v.resize(aligned, 0.0);
+            v
+        } else {
+            state_vec.to_vec()
+        };
 
         // Convert to Tensor using trainer's device (GPU or CPU)
-        Tensor::new(state_vec.as_slice(), &self.device)
+        Tensor::new(padded.as_slice(), &self.device)
             .context("Failed to create state tensor from TradingState")
     }