diff --git a/src/learn/learn.cpp b/src/learn/learn.cpp
index 0b04d034..af867d42 100644
--- a/src/learn/learn.cpp
+++ b/src/learn/learn.cpp
@@ -215,21 +215,28 @@ namespace Learner
         auto loss_ = pow(q_ - p_, 2.0) * (1.0 / (2400.0 * 2.0 * 600.0));
         */
 
-        const double epsilon = 1e-12;
+        constexpr double epsilon = 1e-12;
 
-        auto q_ = sigmoid(VariableParameter<double, 0>{} * winning_probability_coefficient);
-        auto p_ = sigmoid(ConstantParameter<double, 1>{} * winning_probability_coefficient);
-        auto t_ = (ConstantParameter<double, 2>{} + 1.0) * 0.5;
-        auto lambda_ = ConstantParameter<double, 3>{};
-        auto teacher_entropy_ = -(p_ * log(p_ + epsilon) + (1.0 - p_) * log(1.0 - p_ + epsilon));
-        auto outcome_entropy_ = -(t_ * log(t_ + epsilon) + (1.0 - t_) * log(1.0 - t_ + epsilon));
-        auto teacher_loss_ = -(p_ * log(q_) + (1.0 - p_) * log(1.0 - q_));
-        auto outcome_loss_ = -(t_ * log(q_) + (1.0 - t_) * log(1.0 - q_));
-        auto result_ = lambda_ * teacher_loss_ + (1.0 - lambda_) * outcome_loss_;
-        auto entropy_ = lambda_ * teacher_entropy_ + (1.0 - lambda_) * outcome_entropy_;
-        auto loss_ = result_ - entropy_;
+        static thread_local auto q_ = sigmoid(VariableParameter<double, 0>{} * ConstantParameter<double, 4>{});
+        static thread_local auto p_ = sigmoid(ConstantParameter<double, 1>{} * ConstantParameter<double, 4>{});
+        static thread_local auto t_ = (ConstantParameter<double, 2>{} + 1.0) * 0.5;
+        static thread_local auto lambda_ = ConstantParameter<double, 3>{};
+        static thread_local auto teacher_entropy_ = -(p_ * log(p_ + epsilon) + (1.0 - p_) * log(1.0 - p_ + epsilon));
+        static thread_local auto outcome_entropy_ = -(t_ * log(t_ + epsilon) + (1.0 - t_) * log(1.0 - t_ + epsilon));
+        static thread_local auto teacher_loss_ = -(p_ * log(q_) + (1.0 - p_) * log(1.0 - q_));
+        static thread_local auto outcome_loss_ = -(t_ * log(q_) + (1.0 - t_) * log(1.0 - q_));
+        static thread_local auto result_ = lambda_ * teacher_loss_ + (1.0 - lambda_) * outcome_loss_;
+        static thread_local auto entropy_ = lambda_ * teacher_entropy_ + (1.0 - lambda_) * outcome_entropy_;
+        static thread_local auto loss_ = result_ - entropy_;
+
+        auto args = std::tuple(
+            (double)shallow, 
+            (double)teacher_signal, 
+            (double)result, 
+            calculate_lambda(teacher_signal), 
+            winning_probability_coefficient
+        );
 
-        auto args = std::tuple((double)shallow, (double)teacher_signal, (double)result, calculate_lambda(teacher_signal));
         return loss_.eval(args).clamp_grad(max_grad);
     }