diff --git a/src/learn/learn.cpp b/src/learn/learn.cpp
index 109a43ea..e3bfe3a4 100644
--- a/src/learn/learn.cpp
+++ b/src/learn/learn.cpp
@@ -213,9 +213,9 @@ namespace Learner
         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_;
+        static thread_local auto cross_entropy_ = result_ - entropy_;
 
-        return loss_;
+        return cross_entropy_;
     }
 
     template <typename ValueT>
@@ -247,23 +247,27 @@ namespace Learner
         return perf_;
     }
 
-    static ValueWithGrad<double> get_loss(Value shallow, Value teacher_signal, int result, int ply)
+    static ValueWithGrad<double> get_loss_noob(Value shallow, Value teacher_signal, int result, int /* ply */)
     {
         using namespace Learner::Autograd::UnivariateStatic;
 
-        /*
-        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 loss_ = pow(lambda_ * (q_ - p_) + (1.0 - lambda_) * (q_ - t_), 2.0);
-        */
+        static thread_local auto q_ = VariableParameter<double, 0>{};
+        static thread_local auto p_ = ConstantParameter<double, 1>{};
+        static thread_local auto loss_ = pow(q_ - p_, 2.0) * (1.0 / (2400.0 * 2.0 * 600.0));
 
-        /*
-        auto q_ = VariableParameter<double, 0>{};
-        auto p_ = ConstantParameter<double, 1>{};
-        auto loss_ = pow(q_ - p_, 2.0) * (1.0 / (2400.0 * 2.0 * 600.0));
-        */
+        auto args = std::tuple(
+            (double)shallow,
+            (double)teacher_signal,
+            (double)result,
+            calculate_lambda(teacher_signal)
+        );
+
+        return loss_.eval(args);
+    }
+
+    static ValueWithGrad<double> get_loss_cross_entropy(Value shallow, Value teacher_signal, int result, int /* ply */)
+    {
+        using namespace Learner::Autograd::UnivariateStatic;
 
         static thread_local auto q_ = expected_perf_(VariableParameter<double, 0>{});
         static thread_local auto p_ = expected_perf_(scale_score_(ConstantParameter<double, 1>{}));
@@ -281,6 +285,13 @@ namespace Learner
         return loss_.eval(args);
     }
 
+    static auto get_loss(Value shallow, Value teacher_signal, int result, int ply)
+    {
+        using namespace Learner::Autograd::UnivariateStatic;
+
+        return get_loss_cross_entropy(shallow, teacher_signal, result, ply);
+    }
+
     static auto get_loss(
         Value teacher_signal,
         Value shallow,