diff --git a/crates/ml-alpha/src/trainer/perception.rs b/crates/ml-alpha/src/trainer/perception.rs
index 57b9ee683..af28441eb 100644
--- a/crates/ml-alpha/src/trainer/perception.rs
+++ b/crates/ml-alpha/src/trainer/perception.rs
@@ -168,8 +168,7 @@ pub struct PerceptionTrainer {
     // ── LN_a (between Mamba2 stacks; Phase 2B) ──
     // The existing LN sits between m2 and CfC (now called LN_b). LN_a
     // is a separate instance with its own learnable gain/bias.
-    ln_a_gain_d: CudaSlice<f32>,
-    ln_a_bias_d: CudaSlice<f32>,
+    // X4: LN_a weights moved to `self.trunk.ln_a_gain_d` / `ln_a_bias_d`.
     ln_a_stats_d: CudaSlice<f32>,
     /// `[B, K, HIDDEN_DIM]` — LN_a forward output, fed as input to m2.
     ln_a_out_d: GpuTensor,
@@ -806,8 +805,13 @@ impl PerceptionTrainer {
         opt_ln_gain.wd = 0.0;
         let mut opt_ln_bias = AdamW::new(dev, HIDDEN_DIM, cfg.lr_cfc)?;
         opt_ln_bias.wd = 0.0;
-        let ln_a_gain_d = upload(&stream, &ln_gain_init)?;
-        let ln_a_bias_d = upload(&stream, &ln_bias_init)?;
+        // X4: upload LN_a weights into the trunk's slots.
+        stream
+            .memcpy_htod(&ln_gain_init, &mut trunk.ln_a_gain_d)
+            .context("trunk.ln_a_gain_d upload")?;
+        stream
+            .memcpy_htod(&ln_bias_init, &mut trunk.ln_a_bias_d)
+            .context("trunk.ln_a_bias_d upload")?;
         let mut opt_ln_a_gain = AdamW::new(dev, HIDDEN_DIM, cfg.lr_cfc)?;
         opt_ln_a_gain.wd = 0.0;
         let mut opt_ln_a_bias = AdamW::new(dev, HIDDEN_DIM, cfg.lr_cfc)?;
@@ -1073,8 +1077,6 @@ impl PerceptionTrainer {
             mamba2_l2_bwd_scratch,
             mamba2_l2_grads_buffers,
             // LN_a between Mamba2 stacks.
-            ln_a_gain_d,
-            ln_a_bias_d,
             ln_a_stats_d,
             ln_a_out_d,
             grad_ln_a_gain_per_row_d,
@@ -1454,8 +1456,8 @@ impl PerceptionTrainer {
             let mut launch = self.stream.launch_builder(&self.ln_fwd_fn);
             launch
                 .arg(self.mamba2_fwd_scratch.h_enriched_seq.cuda_data())
-                .arg(&self.ln_a_gain_d)
-                .arg(&self.ln_a_bias_d)
+                .arg(&self.trunk.ln_a_gain_d)
+                .arg(&self.trunk.ln_a_bias_d)
                 .arg(&n_rows_ln)
                 .arg(self.ln_a_out_d.data_mut())
                 .arg(&mut self.ln_a_stats_d);
@@ -2075,7 +2077,7 @@ impl PerceptionTrainer {
             let mut launch = self.stream.launch_builder(&self.ln_bwd_fn);
             launch
                 .arg(self.mamba2_fwd_scratch.h_enriched_seq.cuda_data())
-                .arg(&self.ln_a_gain_d)
+                .arg(&self.trunk.ln_a_gain_d)
                 .arg(&self.ln_a_stats_d)
                 .arg(self.mamba2_l2_grads_buffers.d_x_from_in.cuda_data())
                 .arg(&n_rows_ln)
@@ -2284,8 +2286,8 @@ impl PerceptionTrainer {
         self.opt_heads_b_skip.step(&mut self.heads_b_skip_d, &self.grad_heads_b_skip_d)?;
         self.opt_ln_gain.step(&mut self.ln_gain_d, &self.grad_ln_gain_d)?;
         self.opt_ln_bias.step(&mut self.ln_bias_d, &self.grad_ln_bias_d)?;
-        self.opt_ln_a_gain.step(&mut self.ln_a_gain_d, &self.grad_ln_a_gain_d)?;
-        self.opt_ln_a_bias.step(&mut self.ln_a_bias_d, &self.grad_ln_a_bias_d)?;
+        self.opt_ln_a_gain.step(&mut self.trunk.ln_a_gain_d, &self.grad_ln_a_gain_d)?;
+        self.opt_ln_a_bias.step(&mut self.trunk.ln_a_bias_d, &self.grad_ln_a_bias_d)?;
         self.opt_vsn_w.step(&mut self.trunk.vsn_w_d, &self.grad_vsn_w_d)?;
         self.opt_vsn_b.step(&mut self.trunk.vsn_b_d, &self.grad_vsn_b_d)?;
         self.opt_attn_q.step(&mut self.attn_q_d, &self.grad_attn_q_d)?;
@@ -2493,8 +2495,8 @@ impl PerceptionTrainer {
             let mut launch = self.stream.launch_builder(&self.ln_fwd_fn);
             launch
                 .arg(self.mamba2_fwd_scratch.h_enriched_seq.cuda_data())
-                .arg(&self.ln_a_gain_d)
-                .arg(&self.ln_a_bias_d)
+                .arg(&self.trunk.ln_a_gain_d)
+                .arg(&self.trunk.ln_a_bias_d)
                 .arg(&n_rows_ln)
                 .arg(self.ln_a_out_d.data_mut())
                 .arg(&mut self.ln_a_stats_d);