diff --git a/crates/ml/src/cuda_pipeline/experience_kernels.cu b/crates/ml/src/cuda_pipeline/experience_kernels.cu
index 1edb6912b..6990d3d6d 100644
--- a/crates/ml/src/cuda_pipeline/experience_kernels.cu
+++ b/crates/ml/src/cuda_pipeline/experience_kernels.cu
@@ -2936,3 +2936,40 @@ extern "C" __global__ void selectivity_backward(
     }
     atomicAdd(&d_sel_params[SH2], d_z); /* db */
 }
+
+/**
+ * Phase 1: Compute mean of Q-values into a scratch scalar.
+ * Grid: (1, 1, 1), Block: (256, 1, 1).
+ */
+extern "C" __global__ void q_mean_reduce(
+    const float* __restrict__ q_values,
+    float* __restrict__ mean_out,
+    int total)
+{
+    __shared__ float sdata[256];
+    float sum = 0.0f;
+    for (int i = threadIdx.x; i < total; i += 256) {
+        sum += q_values[i];
+    }
+    sdata[threadIdx.x] = sum;
+    __syncthreads();
+    for (int s = 128; s > 0; s >>= 1) {
+        if (threadIdx.x < s) sdata[threadIdx.x] += sdata[threadIdx.x + s];
+        __syncthreads();
+    }
+    if (threadIdx.x == 0) mean_out[0] = sdata[0] / (float)total;
+}
+
+/**
+ * Phase 2: Subtract mean from all Q-values in-place.
+ * Grid: ceil(total/256), Block: 256.
+ */
+extern "C" __global__ void q_mean_subtract(
+    float* __restrict__ q_values,
+    const float* __restrict__ mean_val,
+    int total)
+{
+    int i = blockIdx.x * blockDim.x + threadIdx.x;
+    if (i >= total) return;
+    q_values[i] -= mean_val[0];
+}
diff --git a/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs b/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
index a38722cdf..ee2e6ea32 100644
--- a/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
+++ b/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
@@ -1253,6 +1253,48 @@ impl GpuDqnTrainer {
         self.q_coord_buf.raw_ptr()
     }
 
+    /// Center Q-values in q_out_buf by subtracting their global mean.
+    ///
+    /// Two-phase GPU operation (no CPU roundtrip):
+    ///   Phase 1 (q_mean_reduce):    [B * total_actions] → mean scalar in q_mean_scratch
+    ///   Phase 2 (q_mean_subtract):  q_out_buf[i] -= q_mean_scratch[0]  in-place
+    ///
+    /// Prevents bootstrapping drift (Q-mean 0→+0.47 over 18 epochs observed in
+    /// train-vpb4w). Must be called AFTER compute_expected_q, BEFORE launch_q_attention.
+    pub(crate) fn launch_q_mean_center(&self, batch_size: usize) -> Result<(), MLError> {
+        let total = (batch_size * self.total_actions()) as i32;
+        let q_ptr = self.q_out_buf.raw_ptr();
+        let scratch_ptr = self.q_mean_scratch.raw_ptr();
+        // Phase 1: reduce all Q-values to their mean
+        unsafe {
+            self.stream.launch_builder(&self.q_mean_reduce_kernel)
+                .arg(&q_ptr)
+                .arg(&scratch_ptr)
+                .arg(&total)
+                .launch(LaunchConfig {
+                    grid_dim: (1, 1, 1),
+                    block_dim: (256, 1, 1),
+                    shared_mem_bytes: 0,
+                })
+                .map_err(|e| MLError::ModelError(format!("q_mean_reduce: {e}")))?;
+        }
+        // Phase 2: subtract mean from every Q-value
+        let blocks = ((total as u32 + 255) / 256).max(1);
+        unsafe {
+            self.stream.launch_builder(&self.q_mean_subtract_kernel)
+                .arg(&q_ptr)
+                .arg(&scratch_ptr)
+                .arg(&total)
+                .launch(LaunchConfig {
+                    grid_dim: (blocks, 1, 1),
+                    block_dim: (256, 1, 1),
+                    shared_mem_bytes: 0,
+                })
+                .map_err(|e| MLError::ModelError(format!("q_mean_subtract: {e}")))?;
+        }
+        Ok(())
+    }
+
     /// Accumulate first SH2 columns of d_mag_concat [B, SH2+3] into d_h_s2 [B, SH2].
     pub(crate) fn accumulate_d_h_s2_from_concat(
         &self,
@@ -4777,6 +4819,9 @@ impl GpuDqnTrainer {
                 .map_err(|e| MLError::ModelError(format!("compute_expected_q (q_stats): {e}")))?;
         }
 
+        // Q-mean centering: removes bootstrapping drift before attention and stats.
+        self.launch_q_mean_center(batch_size)?;
+
         // Cross-branch Q attention: q_out_buf → q_coord_buf [B, 12].
         // Runs after compute_expected_q so attention weights train on real Q-values.
         self.launch_q_attention(batch_size)?;
diff --git a/crates/ml/src/trainers/dqn/fused_training.rs b/crates/ml/src/trainers/dqn/fused_training.rs
index 2eb062733..c4a1def9f 100644
--- a/crates/ml/src/trainers/dqn/fused_training.rs
+++ b/crates/ml/src/trainers/dqn/fused_training.rs
@@ -1983,6 +1983,14 @@ impl FusedTrainingCtx {
             .map_err(|e| anyhow::anyhow!("launch_q_attention: {e}"))
     }
 
+    /// Center Q-values in q_out_buf by subtracting their global mean.
+    /// Prevents bootstrapping drift. Must be called after compute_expected_q,
+    /// before launch_q_attention.
+    pub(crate) fn launch_q_mean_center(&self, batch_size: usize) -> Result<()> {
+        self.trainer.launch_q_mean_center(batch_size)
+            .map_err(|e| anyhow::anyhow!("launch_q_mean_center: {e}"))
+    }
+
     /// Selectivity gate forward: sigmoid(dot(W_sel, h_s2) + b_sel) per sample.
     pub(crate) fn launch_selectivity_forward(&self, batch_size: usize) -> Result<()> {
         self.trainer.launch_selectivity_forward(batch_size)