diff --git a/crates/ml/src/cuda_pipeline/expected_q_kernel.cu b/crates/ml/src/cuda_pipeline/expected_q_kernel.cu
index 4be24193c..a6898d716 100644
--- a/crates/ml/src/cuda_pipeline/expected_q_kernel.cu
+++ b/crates/ml/src/cuda_pipeline/expected_q_kernel.cu
@@ -41,9 +41,10 @@ extern "C" __global__ void compute_expected_q(
                              + (long long)(adv_offset + a) * num_atoms;
 
             // Numerically stable log_softmax with PER-ATOM dueling mean subtraction.
-            // Q[j] = V[j] + A[a,j] - mean_a(A[*,j])  (mean over actions, separately per atom)
-            // BUG FIX: was computing a single global mean across ALL atoms AND actions,
-            // which destroyed per-atom structure and made all actions produce identical Q-values.
+            // Q[j] = V[j] + centered[j] where centered = (A - mean) / std for magnitude (d==1).
+            // Standardization in the forward pass must MATCH the loss kernels — otherwise
+            // the network's learned weights produce advantages that favor Small (from early
+            // C51 training) which the raw forward pass reads directly, causing action collapse.
             float max_logit = -1e30f;
             for (int j = 0; j < num_atoms; j++) {
                 float a_mean_j = 0.0f;
@@ -53,7 +54,21 @@ extern "C" __global__ void compute_expected_q(
                     a_mean_j += adv_aa[j];
                 }
                 a_mean_j /= (float)bd;
-                float combined = val[j] + adv[j] - a_mean_j;
+                float centered = adv[j] - a_mean_j;
+                /* Advantage standardization for magnitude branch (d==1):
+                 * divide by std to bound advantage scale, matching loss kernels. */
+                if (d == 1) {
+                    float sq_sum = 0.0f;
+                    for (int aa = 0; aa < bd; aa++) {
+                        const float* adv_aa = b_logits + (long long)i * total_actions * num_atoms
+                                            + (long long)(adv_offset + aa) * num_atoms;
+                        float diff = adv_aa[j] - a_mean_j;
+                        sq_sum += diff * diff;
+                    }
+                    float a_std = sqrtf(sq_sum / (float)bd + 1e-12f);
+                    centered /= (a_std + 1e-6f);
+                }
+                float combined = val[j] + centered;
                 max_logit = fmaxf(max_logit, combined);
             }
             float sum_exp = 0.0f;
@@ -65,7 +80,19 @@ extern "C" __global__ void compute_expected_q(
                     a_mean_j += adv_aa[j];
                 }
                 a_mean_j /= (float)bd;
-                float combined = val[j] + adv[j] - a_mean_j;
+                float centered = adv[j] - a_mean_j;
+                if (d == 1) {
+                    float sq_sum = 0.0f;
+                    for (int aa = 0; aa < bd; aa++) {
+                        const float* adv_aa = b_logits + (long long)i * total_actions * num_atoms
+                                            + (long long)(adv_offset + aa) * num_atoms;
+                        float diff = adv_aa[j] - a_mean_j;
+                        sq_sum += diff * diff;
+                    }
+                    float a_std = sqrtf(sq_sum / (float)bd + 1e-12f);
+                    centered /= (a_std + 1e-6f);
+                }
+                float combined = val[j] + centered;
                 sum_exp += expf(combined - max_logit);
             }
             float log_sum = logf(sum_exp + 1e-8f) + max_logit;
@@ -80,7 +107,19 @@ extern "C" __global__ void compute_expected_q(
                     a_mean_j += adv_aa[j];
                 }
                 a_mean_j /= (float)bd;
-                float combined = val[j] + adv[j] - a_mean_j;
+                float centered = adv[j] - a_mean_j;
+                if (d == 1) {
+                    float sq_sum = 0.0f;
+                    for (int aa = 0; aa < bd; aa++) {
+                        const float* adv_aa = b_logits + (long long)i * total_actions * num_atoms
+                                            + (long long)(adv_offset + aa) * num_atoms;
+                        float diff = adv_aa[j] - a_mean_j;
+                        sq_sum += diff * diff;
+                    }
+                    float a_std = sqrtf(sq_sum / (float)bd + 1e-12f);
+                    centered /= (a_std + 1e-6f);
+                }
+                float combined = val[j] + centered;
                 float p = expf(combined - log_sum);
                 float z = v_min + (float)j * dz;
                 eq += p * z;