diff --git a/crates/ml/src/hyperopt/adapters/dqn.rs b/crates/ml/src/hyperopt/adapters/dqn.rs
index 6fe9ca82a..aa16731dc 100644
--- a/crates/ml/src/hyperopt/adapters/dqn.rs
+++ b/crates/ml/src/hyperopt/adapters/dqn.rs
@@ -2346,10 +2346,12 @@ impl HyperparameterOptimizable for DQNTrainer {
             min_replay_size: params.batch_size * 2, // Need at least 2x batch size
             epochs: self.epochs,
             checkpoint_frequency: (self.epochs / 5).max(1), // Save 5 checkpoints per trial, min 1
-            // C4 FIX: Re-enable early stopping with adaptive plateau window.
-            // Previous issue: plateau_window=5 with 8 epochs triggered too often.
-            // Fix: adaptive window = max(epochs / 2, 3), giving enough learning time.
-            early_stopping_enabled: true,
+            // Disable early stopping for short hyperopt trials (≤12 epochs).
+            // Reason: early stopping returns penalty metrics + no backtest, causing
+            // ALL trials to hit FALLBACK OBJECTIVE. With 8 epochs (~90s per trial),
+            // it's cheaper to run all epochs and get real backtest metrics.
+            // Long training runs (train-best, 50 epochs) still use early stopping.
+            early_stopping_enabled: self.epochs > 12,
             q_value_floor: -5.0,
             min_loss_improvement_pct: 2.0,
             plateau_window: (self.epochs / 2).max(5),