diff --git a/crates/ml/src/trainers/dqn/trainer/metrics.rs b/crates/ml/src/trainers/dqn/trainer/metrics.rs
index e33d1ed3f..9872eb37c 100644
--- a/crates/ml/src/trainers/dqn/trainer/metrics.rs
+++ b/crates/ml/src/trainers/dqn/trainer/metrics.rs
@@ -275,32 +275,24 @@ impl DQNTrainer {
         crate::cuda_pipeline::dtoh_bf16_to_f32(stream, q_out, &mut host_q).ok()?;
         host_q.truncate(sample_size * total_actions);
 
-        // Compute gap (best - second_best) and per-action averages on host
+        // Compute gap (best - second_best) WITHIN DIRECTION BRANCH ONLY
+        // and per-action averages on host.
+        // Q-values layout: [dir_0..dir_2, mag_0..mag_2, ord_0..ord_2, urg_0..urg_2]
+        // The gap measures direction conviction (best_dir - second_best_dir).
         let cols = total_actions;
         let rows = sample_size;
+        let (b0, b1, _, _) = {
+            let ag = self.agent.read().await;
+            ag.branch_sizes()
+        };
         let mut gaps = Vec::with_capacity(rows);
         let mut col_sums = [0.0_f64; 9];
         for r in 0..rows {
             let row_offset = r * cols;
+            // Direction gap: best vs second_best within first b0 columns only
             let mut best_val = f32::NEG_INFINITY;
             let mut second_best = f32::NEG_INFINITY;
-            for c in 0..cols.min(9) {
-                let v = host_q.get(row_offset + c).copied().unwrap_or(0.0);
-                if let Some(s) = col_sums.get_mut(c) {
-                    *s += v as f64;
-                }
-                if v > best_val {
-                    second_best = best_val;
-                    best_val = v;
-                } else if v > second_best {
-                    second_best = v;
-                } else {
-                    // v <= second_best: no update needed
-                }
-            }
-            // Also check remaining columns (9..total_actions) for best/second_best
-            // (order_type + urgency branches, if total_actions > 9)
-            for c in 9..cols {
+            for c in 0..b0.min(cols) {
                 let v = host_q.get(row_offset + c).copied().unwrap_or(0.0);
                 if v > best_val {
                     second_best = best_val;
@@ -310,6 +302,19 @@ impl DQNTrainer {
                 }
             }
             gaps.push(best_val - second_best);
+            // Per-action sums: map branch outputs to 9 dir×mag combos
+            // dir_d × mag_m → index d*b1 + m (for display only)
+            for d in 0..b0.min(3) {
+                let q_dir = host_q.get(row_offset + d).copied().unwrap_or(0.0) as f64;
+                for m in 0..b1.min(3) {
+                    let q_mag = host_q.get(row_offset + b0 + m).copied().unwrap_or(0.0) as f64;
+                    let combo_idx = d * b1.min(3) + m;
+                    if let Some(s) = col_sums.get_mut(combo_idx) {
+                        // Combined Q ≈ Q_dir + Q_mag (additive advantage)
+                        *s += q_dir + q_mag;
+                    }
+                }
+            }
         }
 
         let rows_f64 = rows as f64;