diff --git a/docs/source/al_description.rst b/docs/source/al_description.rst
index ac52ef0a6d1fc427527c94dd991756aa04634ef5..4b3ce9859bfe69203c1c5cc42020faf83244c952 100644
--- a/docs/source/al_description.rst
+++ b/docs/source/al_description.rst
@@ -2,10 +2,10 @@ Active learning: iteratively expanding the training set
 *******************************************************
 Active learning (AL), also called sequential training, is the iterative choice of additional training samples after the initial training of a surrogate model.
 The new samples can be chosen in an explorative manner or by exploiting available data and properties of the surrogate.
+The relevant functions are contained in the class :any:`bayesvalidrox.surrogate_models.sequential_design.SequentialDesign` and :any:`bayesvalidrox.surrogate_models.exploration.Exploration`.
 
 .. warning::
-   The active learning methods are currently being reworked. 
-   This should not change the function call ``Engine.train_sequential()``, but will change the associated class structures.
+   Exploration with 'voronoi' is disabled for release v1.1.0!
 
 .. image:: ../diagrams/active_learning_reduced.png
    :width: 550
@@ -54,6 +54,5 @@ Once all properties are set, we can assemble the engine and start it.
 This time we use ``train_sequential``.
 	
 >>> Engine_ = Engine(MetaMod, Model, ExpDesign)
->>> Engine_.start_engine()
 >>> Engine_.train_sequential()
     
\ No newline at end of file
diff --git a/docs/source/surrogate_description.rst b/docs/source/surrogate_description.rst
index 0d17b06ceb08848c9b4e6471ab331673f8a7c2d3..2a1da4b3fc230aa0fbb98369a5b00edb676a8088 100644
--- a/docs/source/surrogate_description.rst
+++ b/docs/source/surrogate_description.rst
@@ -85,14 +85,9 @@ For this the sampling method should be set to 'user' and our samples given as ``
 >>> ExpDesign.sampling_method = 'user'
 >>> ExpDesign.root_samples = samples
 
-Now we create an engine object with the model, experimental design and surrogate model.
-With the function ``start_engine`` the engine performs its preparations for training.
+Now we create an engine object with the model, experimental design and surrogate model and run the training.
 
 >>> Engine_ = Engine(MetaMod, Model, ExpDesign)
->>> Engine_.start_engine()
-
-Then we train the surrogate model.
-
 >>> Engine.train_normal()
 
 We can evaluate the trained surrogate model in two ways, via the engine, or directly.