diff --git a/docs/docDIAL.md b/docs/docDIAL.md
index 8b8111de8..600f8b2cb 100644
--- a/docs/docDIAL.md
+++ b/docs/docDIAL.md
@@ -72,3 +72,9 @@ This procedure yields to the following availability of hyperparameter:
 - `--dial_epsilon`: pixel wise threshold to perturb images
 - `--gamma_reg`: ? ($\epsilon$ in the paper)
 - `--lr`: learning rate ($\alpha$ in the paper)
+
+# Examples
+
+```
+python main_out.py --te_d=0 --task=mnistcolor10 --model=erm --trainer=dial --nname=conv_bn_pool_2
+```
diff --git a/docs/docFishr.md b/docs/docFishr.md
index 08580d9fe..e2ba4c1b9 100644
--- a/docs/docFishr.md
+++ b/docs/docFishr.md
@@ -72,6 +72,10 @@ For more details, see the reference below or the domainlab code.
 
 
 
+# Examples
+```
+python main_out.py --te_d=0 --task=mini_vlcs --model=erm --trainer=fishr --nname=alexnet --bs=2 --nocu
+```
 
 
 
@@ -79,3 +83,4 @@ _Reference:_
 Rame, Alexandre, Corentin Dancette, and Matthieu Cord. "Fishr:
 Invariant gradient variances for out-of-distribution generalization."
 International Conference on Machine Learning. PMLR, 2022.
+
diff --git a/docs/docHDUVA.md b/docs/docHDUVA.md
index 4abcb71e9..47af4d344 100644
--- a/docs/docHDUVA.md
+++ b/docs/docHDUVA.md
@@ -52,6 +52,18 @@ Alternatively, one could use an existing neural network in DomainLab using `nnam
 
 ## Hyperparameter for warmup
 Finally, the number of epochs for hyper-parameter warm-up can be specified via the argument `warmup`.
+## Examples
+### use hduva on color mnist, train on 2 domains
+```shell
+python main_out.py --tr_d 0 1 2 --te_d 3 --bs=2 --task=mnistcolor10 --model=hduva  --nname=conv_bn_pool_2 --gamma_y=7e5 --nname_encoder_x2topic_h=conv_bn_pool_2 --nname_encoder_sandwich_x2h4zd=conv_bn_pool_2
+```
+
+### hduva is domain-unsupervised, so it works also with a single domain
+```shell
+python main_out.py --tr_d 0  --te_d 3 4 --bs=2 --task=mnistcolor10 --model=hduva --nname=conv_bn_pool_2 --gamma_y=7e5 --nname_encoder_x2topic_h=conv_bn_pool_2 --nname_encoder_sandwich_x2h4zd=conv_bn_pool_2
+```
+
+
 
 Please cite our paper if you find it useful!
 ```text
diff --git a/docs/docIRM.md b/docs/docIRM.md
index 955ff14a3..0b8114de8 100644
--- a/docs/docIRM.md
+++ b/docs/docIRM.md
@@ -26,4 +26,8 @@ where $\lambda$ is a hyperparameter that controls the trade-off between the empi
 In practice, one could simply divide one mini-batch into two subsets, let $i$ and $j$ to index these two subsets, multiply  subset $i$ and subset $j$ forms an unbiased estimation of the L2 norm of gradient.
 In detail: the squared gradient norm via inner product between $\nabla_{w|w=1} \ell(w \circ \Phi(X^{(d, i)}), Y^{(d, i)})$ of dimension dim(Grad) with $\nabla_{w|w=1} \ell(w \circ \Phi(X^{(d, j)}), Y^{(d, j)})$ of dimension dim(Grad) For more details, see section 3.2 and Appendix D of : Arjovsky et al., “Invariant Risk Minimization.”
 
+# Examples
+```shell
+python main_out.py --te_d=0 --task=mnistcolor10 --model=erm --trainer=irm --nname=conv_bn_pool_2
 
+```
diff --git a/docs/doc_examples.md b/docs/doc_examples.md
index 21d0b2eb2..0dddc5f5b 100755
--- a/docs/doc_examples.md
+++ b/docs/doc_examples.md
@@ -26,16 +26,6 @@ python main_out.py --te_d 0 1 --tr_d 3 5 --task=mnistcolor10 --debug --bs=2 --mo
 python main_out.py --te_d=0 --task=mnistcolor10 --keep_model --model=diva --nname=conv_bn_pool_2 --nname_dom=conv_bn_pool_2 --gamma_y=10e5 --gamma_d=1e5 --gen
 ```
 
-### use hduva on color mnist, train on 2 domains
-```shell
-python main_out.py --tr_d 0 1 2 --te_d 3 --bs=2 --task=mnistcolor10 --model=hduva  --nname=conv_bn_pool_2 --gamma_y=7e5 --nname_encoder_x2topic_h=conv_bn_pool_2 --nname_encoder_sandwich_x2h4zd=conv_bn_pool_2
-```
-
-### hduva is domain-unsupervised, so it works also with a single domain
-```shell
-python main_out.py --tr_d 0  --te_d 3 4 --bs=2 --task=mnistcolor10 --model=hduva --nname=conv_bn_pool_2 --gamma_y=7e5 --nname_encoder_x2topic_h=conv_bn_pool_2 --nname_encoder_sandwich_x2h4zd=conv_bn_pool_2
-```
-
 
 ## Larger images:
 
diff --git a/scripts/ci_run_examples.sh b/scripts/ci_run_examples.sh
index 9f6b4e041..98e0046a6 100644
--- a/scripts/ci_run_examples.sh
+++ b/scripts/ci_run_examples.sh
@@ -3,14 +3,24 @@ set -e  # exit upon first error
 # >> append content
 # > erase original content
 
-# echo "#!/bin/bash -x -v" > sh_temp_example.sh
-sed -n '/```shell/,/```/ p' docs/doc_examples.md | sed '/^```/ d' >> ./sh_temp_example.sh
-split -l 5 sh_temp_example.sh sh_example_split
-for file in sh_example_split*;
-do (echo "#!/bin/bash -x -v" > "$file"_exe && cat "$file" >> "$file"_exe && bash -x -v "$file"_exe && rm -r zoutput);
+
+files=("docs/docDIAL.md" "docs/docIRM.md" "docs/doc_examples.md" "docs/docHDUVA.md")
+
+for file in "${files[@]}"
+do
+echo "Processing $file"
+# no need to remove sh_temp_algo.sh since the following line overwrite it each time
+echo "#!/bin/bash -x -v" > sh_temp_algo.sh  
+# remove code marker ```
+# we use >> here to append to keep the header #!/bin/bash -x -v
+sed -n '/```shell/,/```/ p' $file | sed '/^```/ d' >> ./sh_temp_algo.sh
+cat sh_temp_algo.sh
+bash -x -v -e sh_temp_algo.sh
+# Add your commands to process each file here
+echo "finished with $file"
 done
-# bash -x -v -e sh_temp_example.sh
-echo "general examples done"
+
+
 
 echo "#!/bin/bash -x -v" > sh_temp_mnist.sh
 sed -n '/```shell/,/```/ p' docs/doc_MNIST_classification.md | sed '/^```/ d' >> ./sh_temp_mnist.sh