diff --git a/config/training/dqn-hyperopt.toml b/config/training/dqn-hyperopt.toml
index c83ede1f5..96f04e26e 100644
--- a/config/training/dqn-hyperopt.toml
+++ b/config/training/dqn-hyperopt.toml
@@ -87,6 +87,11 @@ q_gap_threshold = [0.0, 0.5]          # 0.0=trade every bar, 0.5=high conviction
 cea_weight = [0.1, 1.0]
 order_credit_weight = [0.0, 0.5]
 risk_efficiency_weight = [0.0, 0.5]
+urgency_credit_weight = [0.0, 0.5]
+exit_timing_weight = [0.0, 0.2]
+ofi_reward_weight = [0.0, 0.5]
+kelly_sizing_weight = [0.0, 0.5]
+reward_noise_scale = [0.01, 0.1]
 
 [experience]
 initial_capital = 100000.0
@@ -114,6 +119,11 @@ q_gap_threshold = 0.1
 cea_weight = 0.3
 order_credit_weight = 0.1
 risk_efficiency_weight = 0.1
+urgency_credit_weight = 0.1
+exit_timing_weight = 0.05
+ofi_reward_weight = 0.2
+kelly_sizing_weight = 0.1
+reward_noise_scale = 0.05
 
 [fixed]
 
@@ -145,3 +155,8 @@ q_gap_threshold = 0.1
 cea_weight = 0.3
 order_credit_weight = 0.1
 risk_efficiency_weight = 0.1
+urgency_credit_weight = 0.1
+exit_timing_weight = 0.05
+ofi_reward_weight = 0.2
+kelly_sizing_weight = 0.1
+reward_noise_scale = 0.05
diff --git a/config/training/dqn-localdev.toml b/config/training/dqn-localdev.toml
index 7b9e0764b..79e3ec8ea 100644
--- a/config/training/dqn-localdev.toml
+++ b/config/training/dqn-localdev.toml
@@ -120,3 +120,8 @@ dd_threshold = 0.02
 cea_weight = 0.3
 order_credit_weight = 0.1
 risk_efficiency_weight = 0.1
+urgency_credit_weight = 0.1
+exit_timing_weight = 0.05
+ofi_reward_weight = 0.2
+kelly_sizing_weight = 0.1
+reward_noise_scale = 0.05
diff --git a/config/training/dqn-production.toml b/config/training/dqn-production.toml
index 29d65673d..f2395a216 100644
--- a/config/training/dqn-production.toml
+++ b/config/training/dqn-production.toml
@@ -131,3 +131,8 @@ dd_threshold = 0.02
 cea_weight = 0.3
 order_credit_weight = 0.1
 risk_efficiency_weight = 0.1
+urgency_credit_weight = 0.1
+exit_timing_weight = 0.05
+ofi_reward_weight = 0.2
+kelly_sizing_weight = 0.1
+reward_noise_scale = 0.05
diff --git a/config/training/dqn-smoketest.toml b/config/training/dqn-smoketest.toml
index fd2406d70..1923865ce 100644
--- a/config/training/dqn-smoketest.toml
+++ b/config/training/dqn-smoketest.toml
@@ -127,6 +127,11 @@ dd_threshold = 0.02
 cea_weight = 0.3
 order_credit_weight = 0.1
 risk_efficiency_weight = 0.1
+urgency_credit_weight = 0.1
+exit_timing_weight = 0.05
+ofi_reward_weight = 0.2
+kelly_sizing_weight = 0.1
+reward_noise_scale = 0.05
 
 [walk_forward]
 step_fraction = 0.25
diff --git a/crates/ml/src/cuda_pipeline/experience_kernels.cu b/crates/ml/src/cuda_pipeline/experience_kernels.cu
index 2dbf5bc92..b2a5ade82 100644
--- a/crates/ml/src/cuda_pipeline/experience_kernels.cu
+++ b/crates/ml/src/cuda_pipeline/experience_kernels.cu
@@ -866,6 +866,11 @@ extern "C" __global__ void experience_env_step(
      * at episode end (done=1) and added to final reward. */
     float* __restrict__ position_histogram,
     float position_entropy_weight,  /* #19: reward += weight * H(histogram). 0=disabled. */
+    float urgency_credit_weight,    /* v7 gem: urgency branch attribution */
+    float exit_timing_weight,       /* v7 gem: exit timing quality */
+    float ofi_reward_weight,        /* v7 gem: OFI-weighted reward confidence */
+    float kelly_sizing_weight,      /* v7 gem: Kelly-optimal sizing signal */
+    float reward_noise_scale,       /* v7 gem: reward label smoothing noise */
     /* #33 Per-episode saboteur params [N, 3]: (spread_mult, fill_prob, slippage_mult).
      * NULL = disabled (use global scalars). When non-NULL, overrides
      * spread_cost, fill_ioc_fill_prob, tx_cost_multiplier per episode. */
@@ -1307,6 +1312,61 @@ extern "C" __global__ void experience_env_step(
             float risk_eff = segment_return / fabsf(intra_trade_max_dd);
             reward += risk_efficiency_weight * fminf(risk_eff, 5.0f);
         }
+
+        /* ── Layer 6: Urgency branch attribution (fill quality) ── */
+        if (urgency_credit_weight > 0.0f && fabsf(position) > 0.001f && entry_price > 0.0f) {
+            float mid_price = (raw_close + raw_next) * 0.5f;
+            float fill_improvement = (position > 0.0f)
+                ? (mid_price - entry_price) / fmaxf(entry_price, 1.0f)
+                : (entry_price - mid_price) / fmaxf(entry_price, 1.0f);
+            float urgency_credit = fmaxf(fill_improvement / vol_proxy, 0.0f);
+            reward += urgency_credit_weight * fminf(urgency_credit, 2.0f);
+        }
+
+        /* ── Layer 7: Exit timing quality ── */
+        if (exit_timing_weight > 0.0f && (exiting_trade || reversing_trade)) {
+            float post_exit_move = (prev_sign > 0)
+                ? (raw_next - raw_close) / fmaxf(raw_close, 1.0f)
+                : (raw_close - raw_next) / fmaxf(raw_close, 1.0f);
+            float timing_quality = -post_exit_move / vol_proxy;
+            reward += exit_timing_weight * asymmetric_soft_clamp(timing_quality * 10.0f) * 0.1f;
+        }
+
+        /* ── Layer 8: OFI-weighted reward confidence ── */
+        if (ofi_reward_weight > 0.0f) {
+            float price_move = (raw_next - raw_close) / fmaxf(raw_close, 1.0f);
+            float move_magnitude = fabsf(price_move) / vol_proxy;
+            float alignment = 0.0f;
+            if (position > 0.001f && price_move > 0.0f) alignment = 1.0f;
+            if (position < -0.001f && price_move < 0.0f) alignment = 1.0f;
+            float confidence_mult = alignment * fminf(move_magnitude, 1.0f);
+            reward *= (1.0f + ofi_reward_weight * confidence_mult);
+        }
+
+        /* ── Layer 9: Kelly-optimal position sizing signal ── */
+        if (kelly_sizing_weight > 0.0f && (win_count + loss_count) >= 5.0f) {
+            float total_trades = win_count + loss_count;
+            float win_rate_k = win_count / total_trades;
+            float avg_win = (win_count > 0.0f) ? (sum_wins / win_count) : 0.001f;
+            float avg_loss = (loss_count > 0.0f) ? (sum_losses / loss_count) : 0.001f;
+            float payoff_ratio = avg_win / fmaxf(avg_loss, 0.0001f);
+            float kelly_f = (payoff_ratio * win_rate_k - (1.0f - win_rate_k)) / fmaxf(payoff_ratio, 0.0001f);
+            kelly_f = fmaxf(fminf(kelly_f, 1.0f), 0.0f);
+            float actual_fraction = fabsf(position) / fmaxf(max_position, 1.0f);
+            float kelly_closeness = 1.0f - fabsf(actual_fraction - kelly_f);
+            kelly_closeness = fmaxf(kelly_closeness, 0.0f);
+            if (segment_return > 0.0f) {
+                reward += kelly_sizing_weight * kelly_closeness * 0.5f;
+            }
+        }
+    }
+
+    /* ── Reward label smoothing (deterministic jitter, all non-zero rewards) ── */
+    if (reward_noise_scale > 0.0f && reward != 0.0f) {
+        unsigned int hash = (unsigned int)(i * 31337 + bar_idx * 7919 + current_t * 1013);
+        hash ^= (hash >> 16); hash *= 0x45d9f3bu; hash ^= (hash >> 16);
+        float pseudo_noise = ((float)(hash & 0xFFFF) / 65535.0f - 0.5f) * 2.0f;
+        reward += reward_noise_scale * pseudo_noise;
     }
 
     /* ---- Drawdown penalty (from trade_physics.cuh) ---- */
diff --git a/crates/ml/src/cuda_pipeline/gpu_experience_collector.rs b/crates/ml/src/cuda_pipeline/gpu_experience_collector.rs
index 11247af53..13ba79c76 100644
--- a/crates/ml/src/cuda_pipeline/gpu_experience_collector.rs
+++ b/crates/ml/src/cuda_pipeline/gpu_experience_collector.rs
@@ -193,6 +193,11 @@ pub struct ExperienceCollectorConfig {
     pub order_credit_weight: f32,
     /// v7: Intra-trade risk efficiency weight (0.0 = disabled, 0.5 = moderate)
     pub risk_efficiency_weight: f32,
+    pub urgency_credit_weight: f32,
+    pub exit_timing_weight: f32,
+    pub ofi_reward_weight: f32,
+    pub kelly_sizing_weight: f32,
+    pub reward_noise_scale: f32,
     /// Drawdown fraction before penalty starts (0.02 = 2%)
     pub dd_threshold: f32,
     /// Drawdown penalty weight (1.0 = full penalty)
@@ -319,6 +324,11 @@ impl Default for ExperienceCollectorConfig {
             cea_weight: 0.0,
             order_credit_weight: 0.0,
             risk_efficiency_weight: 0.0,
+            urgency_credit_weight: 0.0,
+            exit_timing_weight: 0.0,
+            ofi_reward_weight: 0.0,
+            kelly_sizing_weight: 0.0,
+            reward_noise_scale: 0.0,
             dd_threshold: 0.02,
             w_dd: 1.0,
             beta_penalty: 0.0,
@@ -1841,6 +1851,11 @@ impl GpuExperienceCollector {
                     .arg(&mirror_i32)                      // #10 mirror universe
                     .arg(&mut self.position_histogram)     // #19 position entropy histogram
                     .arg(&config.position_entropy_weight)  // #19 position entropy weight
+                    .arg(&config.urgency_credit_weight)
+                    .arg(&config.exit_timing_weight)
+                    .arg(&config.ofi_reward_weight)
+                    .arg(&config.kelly_sizing_weight)
+                    .arg(&config.reward_noise_scale)
                     // #33 Per-episode saboteur params (0 = NULL = disabled)
                     .arg(&{
                         if self.saboteur_active {
diff --git a/crates/ml/src/hyperopt/adapters/dqn.rs b/crates/ml/src/hyperopt/adapters/dqn.rs
index bbc53efb0..e3d47c289 100644
--- a/crates/ml/src/hyperopt/adapters/dqn.rs
+++ b/crates/ml/src/hyperopt/adapters/dqn.rs
@@ -2181,6 +2181,10 @@ impl HyperparameterOptimizable for DQNTrainer {
         info!("  [v7] cea_weight: {:.3} (TOML base, scaled by loss_shaping_intensity={:.3})", hyperparams.cea_weight, params.loss_shaping_intensity);
         info!("  [v7] order_credit_weight: {:.3} (TOML base, scaled by loss_shaping_intensity={:.3})", hyperparams.order_credit_weight, params.loss_shaping_intensity);
         info!("  [v7] risk_efficiency_weight: {:.3} (TOML base, scaled by loss_shaping_intensity={:.3})", hyperparams.risk_efficiency_weight, params.loss_shaping_intensity);
+        info!("  [v7 gems] urgency={:.3} timing={:.3} ofi={:.3} kelly={:.3} noise={:.3}",
+            hyperparams.urgency_credit_weight, hyperparams.exit_timing_weight,
+            hyperparams.ofi_reward_weight, hyperparams.kelly_sizing_weight,
+            hyperparams.reward_noise_scale);
 
         // Derive max_position_absolute from max_leverage using median close price.
         // This replaces the old hardcoded contract-count cap with a leverage-based
diff --git a/crates/ml/src/trainers/dqn/config.rs b/crates/ml/src/trainers/dqn/config.rs
index f649b2548..28c9dde06 100644
--- a/crates/ml/src/trainers/dqn/config.rs
+++ b/crates/ml/src/trainers/dqn/config.rs
@@ -785,6 +785,16 @@ pub struct DQNHyperparameters {
     /// Reward v7: Intra-trade risk efficiency weight.
     /// Rewards clean winning trades with minimal intra-trade drawdown.
     pub risk_efficiency_weight: f64,
+    /// Reward v7 gem: Urgency branch attribution weight.
+    pub urgency_credit_weight: f64,
+    /// Reward v7 gem: Exit timing quality weight.
+    pub exit_timing_weight: f64,
+    /// Reward v7 gem: OFI-weighted reward confidence multiplier.
+    pub ofi_reward_weight: f64,
+    /// Reward v7 gem: Kelly-optimal sizing signal weight.
+    pub kelly_sizing_weight: f64,
+    /// Reward v7 gem: Reward label smoothing noise scale.
+    pub reward_noise_scale: f64,
     /// Position staleness rent per step held
     pub time_decay_rate: f64,
     /// Q-value gap threshold for trade conviction filter.
@@ -1503,6 +1513,11 @@ impl DQNHyperparameters {
         self.cea_weight = (self.cea_weight * li).clamp(0.0, 2.0);
         self.order_credit_weight = (self.order_credit_weight * li).clamp(0.0, 1.0);
         self.risk_efficiency_weight = (self.risk_efficiency_weight * li).clamp(0.0, 1.0);
+        self.urgency_credit_weight = (self.urgency_credit_weight * li).clamp(0.0, 1.0);
+        self.exit_timing_weight = (self.exit_timing_weight * li).clamp(0.0, 0.5);
+        self.ofi_reward_weight = (self.ofi_reward_weight * li).clamp(0.0, 1.0);
+        self.kelly_sizing_weight = (self.kelly_sizing_weight * li).clamp(0.0, 1.0);
+        self.reward_noise_scale = (self.reward_noise_scale * li).clamp(0.0, 0.2);
         self.position_entropy_weight *= li;
         // v6 legacy (zeroed defaults, no-op)
         self.asymmetric_dd_weight *= li;
@@ -1611,6 +1626,11 @@ impl DQNHyperparameters {
             cea_weight: 0.3,
             order_credit_weight: 0.1,
             risk_efficiency_weight: 0.1,
+            urgency_credit_weight: 0.1,
+            exit_timing_weight: 0.05,
+            ofi_reward_weight: 0.2,
+            kelly_sizing_weight: 0.1,
+            reward_noise_scale: 0.05,
             pruning_epoch: 50,                     // #20: compute mask at epoch 50
             pruning_fraction: 0.7,                 // #20: prune 70% of smallest weights
             bottleneck_dim: 2,                     // #31: 2D information compression (gem of gems)
diff --git a/crates/ml/src/trainers/dqn/trainer/training_loop.rs b/crates/ml/src/trainers/dqn/trainer/training_loop.rs
index 164c8ec27..c43912b02 100644
--- a/crates/ml/src/trainers/dqn/trainer/training_loop.rs
+++ b/crates/ml/src/trainers/dqn/trainer/training_loop.rs
@@ -942,9 +942,17 @@ impl DQNTrainer {
             enable_action_masking: self.enable_action_masking,
             curiosity_scale: 1.0,
             loss_aversion: self.hyperparams.loss_aversion as f32,
-            cea_weight: self.hyperparams.cea_weight as f32,
+            cea_weight: {
+                let base = self.hyperparams.cea_weight as f32;
+                if self.current_epoch < 3 { 1.0_f32.max(base) } else { base }
+            },
             order_credit_weight: self.hyperparams.order_credit_weight as f32,
             risk_efficiency_weight: self.hyperparams.risk_efficiency_weight as f32,
+            urgency_credit_weight: self.hyperparams.urgency_credit_weight as f32,
+            exit_timing_weight: self.hyperparams.exit_timing_weight as f32,
+            ofi_reward_weight: self.hyperparams.ofi_reward_weight as f32,
+            kelly_sizing_weight: self.hyperparams.kelly_sizing_weight as f32,
+            reward_noise_scale: self.hyperparams.reward_noise_scale as f32,
             tx_cost_multiplier: {
                 let base = self.hyperparams.transaction_cost_multiplier as f32;
                 if adversarial { base * 2.0 } else { base }