diff --git a/crates/ml-dqn/src/noisy_layers.rs b/crates/ml-dqn/src/noisy_layers.rs
index 4b459b80e..db5e3077f 100644
--- a/crates/ml-dqn/src/noisy_layers.rs
+++ b/crates/ml-dqn/src/noisy_layers.rs
@@ -71,6 +71,12 @@ pub struct NoisyLinear {
     in_features: usize,
     out_features: usize,
 
+    // Task 2.5 Bug #4: effective sigma scale applied by the scheduler via
+    // `reset_noise_with_sigma`. Retained so `sigma_mean()` can report the
+    // *effective* σ (scaled), not the raw σ tensor constant. Default 1.0
+    // means "no scheduler attenuation", matching reset_noise()'s behaviour.
+    current_sigma_scale: f32,
+
     // CUDA stream for GPU operations
     stream: Arc<CudaStream>,
 }
@@ -145,6 +151,7 @@ impl NoisyLinear {
             bias_epsilon,
             in_features,
             out_features,
+            current_sigma_scale: 1.0,
             stream,
         })
     }
@@ -163,6 +170,11 @@ impl NoisyLinear {
         // where f(x) = sign(x) * sqrt(|x|)
         let scale = sigma_scale as f32;
 
+        // Task 2.5 Bug #4: record scheduler attenuation so HEALTH_DIAG
+        // `sigma_mean` reports effective σ (= raw σ × current scale),
+        // not the constant raw weight_sigma tensor mean.
+        self.current_sigma_scale = scale;
+
         // Generate raw Gaussian noise on host, apply f(x) = sign(x) * sqrt(|x|)
         let apply_f = |x: f32| -> f32 {
             let sign = if x >= 0.0 { 1.0_f32 } else { -1.0_f32 };
@@ -339,9 +351,16 @@ impl NoisyLinear {
         [&self.weight_sigma, &self.bias_sigma]
     }
 
-    /// Task 0.6 — mean of |sigma| across weight + bias σ tensors. Used by
-    /// HEALTH_DIAG to track NoisyNets exploration pressure per branch
-    /// (H7 detection signal). Sync stream-readback; epoch-boundary only.
+    /// Task 0.6 — mean of |sigma| across weight + bias σ tensors, scaled by
+    /// the scheduler's current sigma_scale. Used by HEALTH_DIAG to track
+    /// NoisyNets exploration pressure per branch (H7 detection signal).
+    /// Sync stream-readback; epoch-boundary only.
+    ///
+    /// Task 2.5 Bug #4: result is multiplied by `current_sigma_scale` so it
+    /// reports effective σ (raw σ × scheduler scale), not the constant raw
+    /// weight_sigma tensor mean. `reset_noise_with_sigma` scales the noise
+    /// epsilon samples; the σ tensor itself stays fixed, so without this
+    /// multiplier the diag reads constant 0.0320 regardless of schedule.
     pub fn sigma_mean(&self) -> Result<f32, MLError> {
         let n_w = self.weight_sigma.len();
         let n_b = self.bias_sigma.len();
@@ -358,7 +377,8 @@ impl NoisyLinear {
             .map_err(|e| MLError::ModelError(format!("sigma_mean bias dtoh: {e}")))?;
         let sum_w: f64 = h_w.iter().map(|x| x.abs() as f64).sum();
         let sum_b: f64 = h_b.iter().map(|x| x.abs() as f64).sum();
-        Ok(((sum_w + sum_b) / ((n_w + n_b) as f64)) as f32)
+        let raw_mean = ((sum_w + sum_b) / ((n_w + n_b) as f64)) as f32;
+        Ok(raw_mean * self.current_sigma_scale)
     }
 
     /// Disable noise for evaluation (use mean parameters only)
@@ -387,6 +407,9 @@ impl NoisyLinear {
         Self::dtod_copy_async(&src.bias_sigma, &mut self.bias_sigma, "bias_sigma", &self.stream)?;
         Self::dtod_copy_async(&src.weight_epsilon, &mut self.weight_epsilon, "weight_epsilon", &self.stream)?;
         Self::dtod_copy_async(&src.bias_epsilon, &mut self.bias_epsilon, "bias_epsilon", &self.stream)?;
+        // Task 2.5 Bug #4: propagate scheduler sigma scale so a target-net
+        // sync doesn't reset the effective σ reporting to 1.0.
+        self.current_sigma_scale = src.current_sigma_scale;
         Ok(())
     }