diff --git a/crates/ml/src/dqn/dqn.rs b/crates/ml/src/dqn/dqn.rs
index 05e406afd..617f29bc6 100644
--- a/crates/ml/src/dqn/dqn.rs
+++ b/crates/ml/src/dqn/dqn.rs
@@ -3997,4 +3997,120 @@ mod tests {
 
         Ok(())
     }
+
+    #[test]
+    fn test_batch_hierarchical_softmax_actions_smoke() -> anyhow::Result<()> {
+        // Smoke test: hierarchical softmax produces valid actions with full diversity
+        let mut config = DQNConfig::emergency_safe_defaults();
+        config.state_dim = 8;
+        config.num_actions = 45; // 5 exposure × 3 order × 3 urgency
+        config.hidden_dims = vec![32, 32];
+        config.use_distributional = false;
+        config.use_dueling = false;
+        config.use_iqn = false;
+        config.use_cql = false;
+
+        let dqn = DQN::new(config)?;
+
+        // Batch of 100 random states
+        let batch_size = 100;
+        let states = Tensor::rand(0.0_f32, 1.0_f32, &[batch_size, 8], &dqn.device)?;
+
+        // Temperature 1.0: should produce diverse actions
+        let actions = dqn.batch_hierarchical_softmax_actions(&states, 1.0)?;
+        assert_eq!(actions.len(), batch_size, "Should return one action per state");
+
+        // All actions must be in [0, 45)
+        for (i, &a) in actions.iter().enumerate() {
+            assert!(a < 45, "Action {} out of range: {} (expected <45)", i, a);
+        }
+
+        // With temp=1.0 and 100 samples, expect reasonable diversity (at least 5 unique)
+        let unique: std::collections::HashSet<usize> = actions.iter().copied().collect();
+        assert!(
+            unique.len() >= 5,
+            "Expected ≥5 unique actions at temp=1.0, got {}/45: {:?}",
+            unique.len(),
+            unique
+        );
+
+        // Verify exposure coverage: at least 2 of 5 exposure levels used
+        let exposure_levels: std::collections::HashSet<usize> =
+            actions.iter().map(|&a| a / 9).collect();
+        assert!(
+            exposure_levels.len() >= 2,
+            "Hierarchical should cover ≥2 exposure levels, got {}/5: {:?}",
+            exposure_levels.len(),
+            exposure_levels
+        );
+
+        Ok(())
+    }
+
+    #[test]
+    fn test_batch_hierarchical_softmax_low_temp_valid() -> anyhow::Result<()> {
+        // Even at low temperature, outputs must be valid indices
+        let mut config = DQNConfig::emergency_safe_defaults();
+        config.state_dim = 8;
+        config.num_actions = 45;
+        config.hidden_dims = vec![16, 16];
+        config.use_distributional = false;
+        config.use_dueling = false;
+        config.use_iqn = false;
+        config.use_cql = false;
+
+        let dqn = DQN::new(config)?;
+        let states = Tensor::rand(0.0_f32, 1.0_f32, &[50, 8], &dqn.device)?;
+
+        // Very low temp: near-greedy, but still valid
+        let actions = dqn.batch_hierarchical_softmax_actions(&states, 0.01)?;
+        assert_eq!(actions.len(), 50);
+        for &a in &actions {
+            assert!(a < 45, "Low-temp action out of range: {}", a);
+        }
+
+        Ok(())
+    }
+
+    #[test]
+    fn test_batch_hierarchical_vs_flat_softmax_diversity() -> anyhow::Result<()> {
+        // Hierarchical should produce more exposure-level diversity than flat
+        let mut config = DQNConfig::emergency_safe_defaults();
+        config.state_dim = 8;
+        config.num_actions = 45;
+        config.hidden_dims = vec![32, 32];
+        config.use_distributional = false;
+        config.use_dueling = false;
+        config.use_iqn = false;
+        config.use_cql = false;
+
+        let dqn = DQN::new(config)?;
+
+        // Large batch for statistical power
+        let states = Tensor::rand(0.0_f32, 1.0_f32, &[500, 8], &dqn.device)?;
+
+        let hierarchical = dqn.batch_hierarchical_softmax_actions(&states, 0.5)?;
+        let flat = dqn.batch_softmax_actions(&states, 0.5)?;
+
+        // Both must return valid actions
+        assert_eq!(hierarchical.len(), 500);
+        assert_eq!(flat.len(), 500);
+
+        // Count exposure-level coverage
+        let hier_exposures: std::collections::HashSet<usize> =
+            hierarchical.iter().map(|&a| a / 9).collect();
+        let flat_exposures: std::collections::HashSet<usize> =
+            flat.iter().map(|&a| a / 9).collect();
+
+        // Hierarchical should cover at least as many exposure levels
+        // (this is statistical, not guaranteed, but with 500 samples at temp=0.5 it's reliable)
+        assert!(
+            hier_exposures.len() >= flat_exposures.len(),
+            "Hierarchical ({}/5 exposures) should cover ≥ flat ({}/5)",
+            hier_exposures.len(),
+            flat_exposures.len()
+        );
+
+        Ok(())
+    }
 }
diff --git a/crates/ml/src/hyperopt/adapters/dqn.rs b/crates/ml/src/hyperopt/adapters/dqn.rs
index 1d02f1af1..87a545945 100644
--- a/crates/ml/src/hyperopt/adapters/dqn.rs
+++ b/crates/ml/src/hyperopt/adapters/dqn.rs
@@ -4033,4 +4033,86 @@ mod tests {
             );
         }
     }
+
+    #[test]
+    fn test_eval_softmax_temp_floor_is_half() {
+        // After raising the floor, verify bounds enforce [0.5, 2.0]
+        let bounds = DQNParams::continuous_bounds();
+        let temp_bounds = bounds[26]; // eval_softmax_temp is param 26
+        let min_temp = temp_bounds.0.exp();
+        let max_temp = temp_bounds.1.exp();
+        assert!(
+            (min_temp - 0.5).abs() < 0.01,
+            "eval_softmax_temp min should be ~0.5, got {:.4}",
+            min_temp
+        );
+        assert!(
+            (max_temp - 2.0).abs() < 0.01,
+            "eval_softmax_temp max should be ~2.0, got {:.4}",
+            max_temp
+        );
+
+        // from_continuous should clamp to 0.5 even if value is below
+        let mut low_temp_vec = vec![0.0_f64; 27];
+        // Set all to midpoint of bounds
+        for i in 0..27 {
+            low_temp_vec[i] = (bounds[i].0 + bounds[i].1) / 2.0;
+        }
+        low_temp_vec[26] = 0.01_f64.ln(); // Way below floor
+        let params = DQNParams::from_continuous(&low_temp_vec).unwrap();
+        assert!(
+            params.eval_softmax_temp >= 0.5,
+            "eval_softmax_temp should be clamped to ≥0.5, got {}",
+            params.eval_softmax_temp
+        );
+    }
+
+    #[test]
+    fn test_diversity_short_circuit_blocks_phantom_sharpe() {
+        // Verify that <10 unique actions produces a positive penalty objective,
+        // regardless of how high the Sharpe ratio is (phantom Sharpe=2317 bug)
+        let metrics = DQNMetrics {
+            train_loss: 0.1,
+            val_loss: 0.05,
+            avg_q_value: 0.5,
+            final_epsilon: 0.0,
+            epochs_completed: 8,
+            avg_episode_reward: 100.0,
+            buy_action_pct: 0.5,
+            sell_action_pct: 0.5,
+            hold_action_pct: 0.0,
+            gradient_norm: 0.1,
+            q_value_std: 0.1,
+            backtest_metrics: Some(BacktestMetrics {
+                sharpe_ratio: 2317.0, // Phantom Sharpe!
+                win_rate: 100.0,
+                max_drawdown_pct: 0.0,
+                total_return_pct: 89000.0,
+                total_trades: 27000,
+                sortino_ratio: 22000.0,
+                calmar_ratio: 1_900_000.0,
+                var_95: 0.03,
+                cvar_95: 0.03,
+                beta: 0.0,
+                alpha: 0.0,
+                information_ratio: 0.0,
+                omega_ratio: 232000.0,
+                unique_actions: 2, // Only 2/45 — degenerate!
+            }),
+        };
+
+        let objective = <DQNTrainer as HyperparameterOptimizable>::extract_objective(&metrics);
+        // Must be POSITIVE (bad) — the diversity short-circuit should override the composite
+        assert!(
+            objective > 0.0,
+            "Degenerate trial (2/45 actions) should have positive objective, got {:.4}",
+            objective
+        );
+        // Should be in the 5-10 range (graduated penalty), not -369000
+        assert!(
+            objective < 20.0,
+            "Penalty should be moderate (5-10), got {:.4}",
+            objective
+        );
+    }
 }