diff --git a/config/ml/alpha_compose_backtest.json b/config/ml/alpha_compose_backtest.json
index 5b796bfaf..00692b8a6 100644
--- a/config/ml/alpha_compose_backtest.json
+++ b/config/ml/alpha_compose_backtest.json
@@ -1,424 +1,424 @@
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       "win_rate": 0.05000000074505806
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     {
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+      "avg_n_trades": 285.8933410644531,
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       "n_episodes": 300,
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+      "win_rate": 0.02666666731238365
+    },
+    {
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+      "std_reward": 11.804777145385742,
+      "threshold": 0.10000000149011612,
       "win_rate": 0.006666666828095913
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     {
-      "avg_n_trades": 136.05332946777344,
+      "avg_n_trades": 280.7466735839844,
       "cost": 0.5,
-      "mean_reward": -21.997268676757812,
+      "mean_reward": -44.50050354003906,
       "n_episodes": 300,
-      "p05": -41.8651237487793,
-      "p50": -20.465248107910156,
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-      "sharpe_per_episode": -1.9169398546218872,
-      "std_reward": 11.475200653076172,
-      "threshold": 0.25,
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+      "sharpe_per_episode": -3.4876816272735596,
+      "std_reward": 12.7593355178833,
+      "threshold": 0.10000000149011612,
+      "win_rate": 0.0
+    },
+    {
+      "avg_n_trades": 223.94000244140625,
+      "cost": 0.0,
+      "mean_reward": -5.630343914031982,
+      "n_episodes": 300,
+      "p05": -23.06067657470703,
+      "p50": -3.5636019706726074,
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+      "sharpe_per_episode": -0.6190073490142822,
+      "std_reward": 9.095762252807617,
+      "threshold": 0.15000000596046448,
+      "win_rate": 0.1733333319425583
+    },
+    {
+      "avg_n_trades": 224.2066650390625,
+      "cost": 0.0625,
+      "mean_reward": -7.762266159057617,
+      "n_episodes": 300,
+      "p05": -23.935901641845703,
+      "p50": -5.957376480102539,
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+      "std_reward": 8.51341438293457,
+      "threshold": 0.15000000596046448,
+      "win_rate": 0.09000000357627869
+    },
+    {
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+      "cost": 0.125,
+      "mean_reward": -13.033651351928711,
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+      "p05": -32.822052001953125,
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+      "threshold": 0.15000000596046448,
+      "win_rate": 0.009999999776482582
+    },
+    {
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+      "cost": 0.25,
+      "mean_reward": -21.03263282775879,
+      "n_episodes": 300,
+      "p05": -40.14529800415039,
+      "p50": -19.439125061035156,
+      "p95": -8.333877563476562,
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+      "std_reward": 10.41439151763916,
+      "threshold": 0.15000000596046448,
+      "win_rate": 0.006666666828095913
+    },
+    {
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+      "cost": 0.5,
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+      "n_episodes": 300,
+      "p05": -55.04161834716797,
+      "p50": -31.479198455810547,
+      "p95": -15.453825950622559,
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+      "std_reward": 14.093565940856934,
+      "threshold": 0.15000000596046448,
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+    },
+    {
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+    },
+    {
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+    },
+    {
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+    {
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+    },
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       "win_rate": 0.0033333334140479565
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+    },
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+    },
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+      "threshold": 0.25,
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+    },
+    {
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   "cost_grid": [
diff --git a/crates/ml/examples/alpha_compose_backtest.rs b/crates/ml/examples/alpha_compose_backtest.rs
index 82c51b83d..8e66e05ac 100644
--- a/crates/ml/examples/alpha_compose_backtest.rs
+++ b/crates/ml/examples/alpha_compose_backtest.rs
@@ -97,6 +97,14 @@ struct Cli {
     /// Training-time cost (the policy LEARNED against this cost).
     #[arg(long, default_value_t = 0.0625)]
     train_cost: f32,
+    /// Training-time alpha-confidence threshold for the gate. Forces
+    /// Wait during training when `|sigmoid(alpha)−0.5| < this`, so the
+    /// Q-network learns weights for the gated policy class. Default
+    /// 0.39 — the equilibrium the controller stabilized to in the
+    /// Phase E.2 smoke (alpha_dqn_h600_smoke at ep 200+). Set to 0.0
+    /// for the original ungated-training behavior.
+    #[arg(long, default_value_t = 0.39)]
+    train_threshold: f32,
     /// SGD learning rate during training.
     #[arg(long, default_value_t = 1.0e-4)]
     lr: f32,
@@ -323,7 +331,18 @@ fn main() -> Result<()> {
             }
             stream.synchronize()?;
             let q_host = stream.clone_dtoh(&single_q_dev)?;
-            let action = epsilon_greedy(&q_host, eps, &mut episode_rng);
+            // Phase E.3: gate during training so the Q-network learns the
+            // value function for the gated policy class. Using a FIXED
+            // threshold (--train-threshold) keeps the backtest self-contained
+            // — no controller invocation needed. The default 0.39 is the
+            // equilibrium the smoke's controller stabilized on.
+            let action = epsilon_greedy_gated(
+                &q_host,
+                s_vec[1],
+                cli.train_threshold,
+                eps,
+                &mut episode_rng,
+            );
             let (_s_next, reward, done) = env
                 .step(action, &mut state)
                 .ok_or_else(|| anyhow::anyhow!("step returned None"))?;