diff --git a/docs/src/tutorials/low_level.md b/docs/src/tutorials/low_level.md
index 702afe90c..06fc1faff 100644
--- a/docs/src/tutorials/low_level.md
+++ b/docs/src/tutorials/low_level.md
@@ -37,7 +37,7 @@ domains = [t ∈ Interval(0.0, 1.0),
 
 # Neural network
 chain = Lux.Chain(Dense(2, 16, Lux.σ), Dense(16, 16, Lux.σ), Dense(16, 1))
-strategy = NeuralPDE.QuadratureTraining
+strategy = NeuralPDE.QuadratureTraining()
 
 indvars = [t, x]
 depvars = [u(t, x)]
diff --git a/docs/src/tutorials/neural_adapter.md b/docs/src/tutorials/neural_adapter.md
index e8b65d8e7..db4ac9574 100644
--- a/docs/src/tutorials/neural_adapter.md
+++ b/docs/src/tutorials/neural_adapter.md
@@ -67,7 +67,7 @@ function loss(cord, θ)
     chain2(cord, θ) .- phi(cord, res.u)
 end
 
-strategy = NeuralPDE.GridTraining(0.02)
+strategy = NeuralPDE.QuadratureTraining()
 
 prob_ = NeuralPDE.neural_adapter(loss, init_params2, pde_system, strategy)
 callback = function (p, l)
@@ -179,7 +179,7 @@ for i in 1:count_decomp
     bcs_ = create_bcs(domains_[1].domain, phi_bound)
     @named pde_system_ = PDESystem(eq, bcs_, domains_, [x, y], [u(x, y)])
     push!(pde_system_map, pde_system_)
-    strategy = NeuralPDE.GridTraining([0.1 / count_decomp, 0.1])
+    strategy = NeuralPDE.QuadratureTraining()
 
     discretization = NeuralPDE.PhysicsInformedNN(chains[i], strategy;
                                                  init_params = init_params[i])
@@ -243,10 +243,10 @@ callback = function (p, l)
 end
 
 prob_ = NeuralPDE.neural_adapter(losses, init_params2, pde_system_map,
-                                 NeuralPDE.GridTraining([0.1 / count_decomp, 0.1]))
+                                 NeuralPDE.QuadratureTraining())
 res_ = Optimization.solve(prob_, BFGS(); callback = callback, maxiters = 2000)
 prob_ = NeuralPDE.neural_adapter(losses, res_.minimizer, pde_system_map,
-                                 NeuralPDE.GridTraining([0.05 / count_decomp, 0.05]))
+                                 NeuralPDE.QuadratureTraining())
 res_ = Optimization.solve(prob_, BFGS(); callback = callback, maxiters = 1000)
 
 phi_ = NeuralPDE.get_phi(chain2)
diff --git a/docs/src/tutorials/pdesystem.md b/docs/src/tutorials/pdesystem.md
index b140dc793..4ace039bd 100644
--- a/docs/src/tutorials/pdesystem.md
+++ b/docs/src/tutorials/pdesystem.md
@@ -23,7 +23,7 @@ on the space domain:
 x \in [0, 1] \, , \ y \in [0, 1] \, ,
 ```
 
-with grid discretization `dx = 0.05` using physics-informed neural networks.
+Using physics-informed neural networks.
 
 ## Copy-Pasteable Code
 
@@ -52,7 +52,7 @@ chain = Lux.Chain(Dense(dim, 16, Lux.σ), Dense(16, 16, Lux.σ), Dense(16, 1))
 
 # Discretization
 dx = 0.05
-discretization = PhysicsInformedNN(chain, GridTraining(dx))
+discretization = PhysicsInformedNN(chain, QuadratureTraining())
 
 @named pde_system = PDESystem(eq, bcs, domains, [x, y], [u(x, y)])
 prob = discretize(pde_system, discretization)
@@ -122,9 +122,8 @@ Here, we build PhysicsInformedNN algorithm where `dx` is the step of discretizat
 `strategy` stores information for choosing a training strategy.
 
 ```@example poisson
-# Discretization
 dx = 0.05
-discretization = PhysicsInformedNN(chain, GridTraining(dx))
+discretization = PhysicsInformedNN(chain, QuadratureTraining())
 ```
 
 As described in the API docs, we now need to define the `PDESystem` and create PINNs