diff --git a/docs/source/gefest/quickstart.rst b/docs/source/gefest/quickstart.rst
index 8cee123c2..8f68694e0 100644
--- a/docs/source/gefest/quickstart.rst
+++ b/docs/source/gefest/quickstart.rst
@@ -62,7 +62,7 @@ Let's take a step-by-step look at how to do this.
     from gefest.core.opt.objective.objective import Objective
     from gefest.tools.estimators.estimator import Estimator
 
--  **Step 1**. Define objectives using loss function and simulator of the physical process if required.
+-  **Step 1**. Define objectives using fitness function and simulator of the physical process if required.
 
 Objective for finding a polygon that seems like circle showed below.
 
@@ -127,7 +127,7 @@ Domain describes geometric constraints for individuals.
 
 By default, the standard sampler is used.
 You can select another sampler or define custom for spicific task.
-How to define your own samler described in the tutorials section of the documentation.
+How to define your own sampler described in the tutorials section of the documentation.
 
 -  **Step 4**. Define tuner configuraton.
 
@@ -197,7 +197,7 @@ To know more about configuration options see :ref:`configuration` section of API
 -  **Step 5**. Run generative design and results visualisation. 
 
 Now you can run the optimization as it was described above in *How to run* section of this tutorial.
-Let's look at how it works inside.
+Let's take a look at code in `run_experiments.py` script.
 
 .. code:: python
 
@@ -221,7 +221,7 @@ Let's look at how it works inside.
 
     # Optimized pop visualization
     logger.info('Collecting plots of optimized structures...')
-    # GIFMaker object creates mp4 series of optimized structures plots
+    # GIFMaker object creates mp4 from optimized structures plots
     gm = GIFMaker(domain=opt_params.domain)
     for st in tqdm(optimized_pop):
         gm.create_frame(st, {'Optimized': st.fitness})
diff --git a/docs/source/tutorials/optimisation.rst b/docs/source/tutorials/optimisation.rst
index abfb3e1f1..95b538c1d 100644
--- a/docs/source/tutorials/optimisation.rst
+++ b/docs/source/tutorials/optimisation.rst
@@ -7,7 +7,7 @@ Optimisation
 Optimisers summary
 ------------------
 
-To solve the optimisation problem, 4 optimisers are available in GEFEST - 1 native and 2 based on GOLEM.
+To solve the optimisation problems 4 optimisers are available in GEFEST - 1 native and 2 based on GOLEM.
 All of them have a single interface and can be imported from ``gefest.tools.optimizers``.
 
 .. list-table:: Optimizers comparation
@@ -39,13 +39,13 @@ All of them have a single interface and can be imported from ``gefest.tools.opti
      - :raw-html-m2r:`<code style="background:green;color:white;font-weight:bold">Yes</code>`
 
 
-``BaseGA`` implements the base genetic algorithm, that performs generation of the initial population,
+``BaseGA`` implements the base genetic algorithm, that performs sampling of the initial population,
 crossover and mutation operations, fitness estimation and selection.
 Each of the steps is encapsulated in a separate executor, which allows you to change the logic of individual steps.
 Thus, BaseGA essentially only implements the sequence of their call.
 
-``StandardOptimizer`` is a wrapper for GOLEM`s ``EvoGraphOptimizer`` optimiser.
-It allows to select different evolutionary schemes, adaptive mutation strategies and some other features.
+``StandardOptimizer`` is a wrapper for GOLEM`s ``EvoGraphOptimizer``.
+It allows to use different evolutionary schemes, adaptive mutation strategies and some other features.
 To use multiobjective optimisation set `golem_selection_type` in ``OptimizationParams`` config to 'spea2'.
 
 ``SurrogateOptimizer`` is the extension of ``StandardOptimizer`` with the ability 
@@ -64,7 +64,7 @@ How to optimise
 
 Easiest way to run optimiser described in :ref:`quickstart`.
 
-If you want to get some more control you can do it in code by import corresponding classes:
+If you want to get some more control you can do it in your code:
 
 .. code-block:: python
 
diff --git a/docs/source/tutorials/tuning.rst b/docs/source/tutorials/tuning.rst
index eb02cc0b6..a68059623 100644
--- a/docs/source/tutorials/tuning.rst
+++ b/docs/source/tutorials/tuning.rst
@@ -62,11 +62,12 @@ Here we will take a closer look at several ``TunerParams`` attributes which may
 
 * 
   ``hyperopt_dist`` is the type of distribution from which random values will be taken during tuning. Available values are names of `hpyeropt hp module finctions <https://github.com/hyperopt/hyperopt/blob/master/hyperopt/hp.py>`_.
+  **Note**: in GOLEM 0.4.0 part of tuners does not converts bounds into mu and sigma. Use 'uniform' to avoid invalid intervals. 
 
 * 
   ``variance_generator`` is function that generates bounds of intervals from which random values should pe picked for all components of all point in structure. If normal distribution set they will be automatically converted into means and varicances.
 
-``verage_edge_variance`` function setes variance to 50% of average edge length for each polygon. This solution can be much more "greedy" than necessary, which can lead to many invalid intermediate variants during tuning. To improve fitness in fewer tuning steps, it is worth creating variance generation functions for selecting smaller intervals based on the conditions of a specific task.
+``percent_edge_variance`` function setes variance to spicific percent of average edge length for each polygon. This solution can be much more "greedy" than necessary, which can lead to many invalid intermediate variants during tuning. To improve fitness in fewer tuning steps, it is worth creating variance generation functions for selecting smaller intervals based on the conditions of a specific task.
 
 Now that the ``OptimizationParams`` have been defined and some structures have been created, we can run tuning with couple lines of code: