diff --git a/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs b/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
index 484842d12..cbdfa199e 100644
--- a/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
+++ b/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
@@ -323,12 +323,6 @@ pub struct GpuDqnTrainConfig {
     /// market features from portfolio+MTF+OFI features in the state vector.
     /// OFI features (8 dims, when enabled) bypass the bottleneck via portfolio_dim.
     pub market_dim: usize,
-    /// Gradient budget fraction for IQN. Default 0.40.
-    pub iqn_grad_budget: f32,
-    /// Gradient budget fraction for CQL. Default 0.10.
-    pub cql_grad_budget: f32,
-    /// Gradient budget fraction for ensemble. Default 0.05.
-    pub ens_grad_budget: f32,
 }
 
 impl Default for GpuDqnTrainConfig {
@@ -377,9 +371,6 @@ impl Default for GpuDqnTrainConfig {
             enable_gradient_vaccine: true,     // always on
             bottleneck_dim: 16,
             market_dim: 42, // Default: 42 base features. Overridden to 50 when OFI (MBP-10) enabled.
-            iqn_grad_budget: 0.40,
-            cql_grad_budget: 0.10,
-            ens_grad_budget: 0.05,
         }
     }
 }
diff --git a/crates/ml/src/trainers/dqn/config.rs b/crates/ml/src/trainers/dqn/config.rs
index d1530bcce..0c6efb783 100644
--- a/crates/ml/src/trainers/dqn/config.rs
+++ b/crates/ml/src/trainers/dqn/config.rs
@@ -932,17 +932,6 @@ pub struct DQNHyperparameters {
     /// L_total = L_c51 + iqn_lambda * L_iqn
     /// Range [0.0, 2.0]: 0.0 = C51 only, 0.5 = balanced, 1.0 = equal weight
     pub iqn_lambda: f64,
-    /// Gradient budget fraction for IQN auxiliary objective.
-    /// Controls how much of the total gradient norm IQN consumes.
-    /// Range [0.1, 0.8]. Default 0.40. Higher = IQN dominates (good for magnitude sizing).
-    /// At 0.75, IQN starves C51 — directional learning flatlines.
-    pub iqn_grad_budget: f64,
-    /// Gradient budget fraction for CQL regularization.
-    /// Range [0.0, 0.3]. Default 0.10.
-    pub cql_grad_budget: f64,
-    /// Gradient budget fraction for ensemble diversity.
-    /// Range [0.0, 0.2]. Default 0.05. C51 gets the remainder.
-    pub ens_grad_budget: f64,
     /// Spectral norm σ_max — constrains ||W||_σ ≤ σ_max.
     /// Range [1.0, 10.0]. Default 3.0 (permits Xavier scaling, prevents Q-explosion).
     pub spectral_norm_sigma_max: f64,
@@ -1554,9 +1543,6 @@ impl DQNHyperparameters {
             num_quantiles: 32,                    // Default: 32 quantiles
             qr_kappa: 1.0,                        // Default: 1.0 (standard quantile Huber loss)
             iqn_lambda: 0.25,                     // Default: mild IQN regularization alongside C51
-            iqn_grad_budget: 0.40,                // Default: balanced — was 0.75 (starved C51 directional learning)
-            cql_grad_budget: 0.10,                // Default: mild CQL regularization
-            ens_grad_budget: 0.05,                // Default: ensemble diversity. C51 gets remainder (0.45)
             spectral_norm_sigma_max: 3.0,        // Default: permits Xavier scaling [1.0, 10.0]
             spectral_decoupling_lambda: 0.01,    // Default: mild logit magnitude penalty (Pezeshki 2021)
 
diff --git a/crates/ml/src/trainers/dqn/fused_training.rs b/crates/ml/src/trainers/dqn/fused_training.rs
index b84d36816..23da8a9db 100644
--- a/crates/ml/src/trainers/dqn/fused_training.rs
+++ b/crates/ml/src/trainers/dqn/fused_training.rs
@@ -139,7 +139,6 @@ unsafe impl Sync for ParentGraph {}
 
 /// Per-component gradient norm budget fractions for auxiliary objectives.
 /// C51 gets whatever remains: `1.0 - sum(active_auxiliary_budgets)`.
-/// Configurable via DQNHyperparameters (iqn_grad_budget, cql_grad_budget, ens_grad_budget).
 /// Default: IQN=40%, CQL=10%, ENS=5%, C51=45%.
 /// Old hardcoded: IQN=75% — starved C51 directional learning (grad_norm frozen at 0.655).
 pub(crate) struct GradBudget {
@@ -410,9 +409,6 @@ impl FusedTrainingCtx {
             enable_gradient_vaccine: true,
             bottleneck_dim: hyperparams.bottleneck_dim,
             market_dim: 42, // Always 42 base market features — OFI features bypass bottleneck via portfolio_dim
-            iqn_grad_budget: hyperparams.iqn_grad_budget as f32,
-            cql_grad_budget: hyperparams.cql_grad_budget as f32,
-            ens_grad_budget: hyperparams.ens_grad_budget as f32,
         };
 
         // Create weight set pointer views AFTER GpuDqnTrainer is constructed below.
diff --git a/crates/ml/src/training_profile.rs b/crates/ml/src/training_profile.rs
index 88e3b674f..6a09c4245 100644
--- a/crates/ml/src/training_profile.rs
+++ b/crates/ml/src/training_profile.rs
@@ -399,9 +399,6 @@ pub struct SearchSpaceSection {
     pub branch_hidden_dim: Option<[f64; 2]>,
     pub gradient_accumulation_steps: Option<[f64; 2]>,
     pub iqn_lambda: Option<[f64; 2]>,
-    pub iqn_grad_budget: Option<[f64; 2]>,
-    pub cql_grad_budget: Option<[f64; 2]>,
-    pub ens_grad_budget: Option<[f64; 2]>,
     /// Spectral norm sigma max bounds.
     pub spectral_norm_sigma_max: Option<[f64; 2]>,
     /// C51 warmup epochs bounds.
@@ -571,9 +568,6 @@ impl HyperoptProfile {
             "branch_hidden_dim" => ss.branch_hidden_dim,
             "gradient_accumulation_steps" => ss.gradient_accumulation_steps,
             "iqn_lambda" => ss.iqn_lambda,
-            "iqn_grad_budget" => ss.iqn_grad_budget,
-            "cql_grad_budget" => ss.cql_grad_budget,
-            "ens_grad_budget" => ss.ens_grad_budget,
             "spectral_norm_sigma_max" => ss.spectral_norm_sigma_max,
             "c51_warmup_epochs" => ss.c51_warmup_epochs,
             "c51_alpha_max" => ss.c51_alpha_max,