diff --git a/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs b/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
index 5028f8385..3fd81aa1d 100644
--- a/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
+++ b/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
@@ -1012,15 +1012,21 @@ impl GpuDqnTrainer {
     /// Update eval v_range from observed Q-value statistics.
     /// Called at epoch boundary after compute_q_stats.
     pub fn update_eval_v_range(&mut self, q_mean: f32, q_std: f32) {
-        // EMA-smooth Q-stats to prevent eval v_range from jumping between steps.
-        // Without smoothing, per-sample IQL support produces noisy Q-stats that
-        // cause the backtest atom placement to fluctuate → inconsistent val_Sharpe.
+        // Adaptive-rate EMA: tracks fast when Q-values shift, smooths when stable.
+        // Alpha = prediction_error / (prediction_error + baseline). When the EMA
+        // is far from the current value (shift detected), alpha → 0.5 (fast).
+        // When close (stable), alpha → 0.01 (smooth). Self-calibrating.
         if self.eval_q_mean_ema == 0.0 && self.eval_q_std_ema == 0.0 {
             self.eval_q_mean_ema = q_mean;
             self.eval_q_std_ema = q_std;
         } else {
-            self.eval_q_mean_ema = 0.95 * self.eval_q_mean_ema + 0.05 * q_mean;
-            self.eval_q_std_ema = 0.95 * self.eval_q_std_ema + 0.05 * q_std;
+            let mean_err = (q_mean - self.eval_q_mean_ema).abs();
+            let std_err = (q_std - self.eval_q_std_ema).abs();
+            let baseline = 0.01_f32;
+            let alpha_mean = (mean_err / (mean_err + baseline)).clamp(0.01, 0.5);
+            let alpha_std = (std_err / (std_err + baseline)).clamp(0.01, 0.5);
+            self.eval_q_mean_ema = (1.0 - alpha_mean) * self.eval_q_mean_ema + alpha_mean * q_mean;
+            self.eval_q_std_ema = (1.0 - alpha_std) * self.eval_q_std_ema + alpha_std * q_std;
         }
 
         let half = (3.0 * self.eval_q_std_ema + 1.0).max(1.0);