diff --git a/artifacts/results/images/mnist_eccco.png b/artifacts/results/images/mnist_eccco.png
index 8f18c4d1c8fedef587e36d2d0e59fc724fa840de..c9cd7a83ff98b28026e4ed8b0ff09c5eceee55c6 100644
Binary files a/artifacts/results/images/mnist_eccco.png and b/artifacts/results/images/mnist_eccco.png differ
diff --git a/notebooks/mnist.qmd b/notebooks/mnist.qmd
index 93d5d668d73d0895f08f6ae48f80c5611f0c7dff..c630e681746e78c65dda4db8c1b05553401b2546 100644
--- a/notebooks/mnist.qmd
+++ b/notebooks/mnist.qmd
@@ -320,7 +320,7 @@ model_performance
 ```{julia}
 # ECCCo:
 λ=[0.5,0.1,0.5]
-temp=0.1
+temp=0.5
 η=0.01
 
 # Generate counterfactuals using ECCCo generator:
diff --git a/paper/paper.pdf b/paper/paper.pdf
index 828c613ca85465b91d18e1fc904c22c1cef04531..cc649cace729a26630523e4fdb4cd01f5c23132f 100644
Binary files a/paper/paper.pdf and b/paper/paper.pdf differ
diff --git a/paper/paper.tex b/paper/paper.tex
index 663707bc3de3fe959e99901b0341701381a86205..4baac709ac24be94735335b3c9c23de1eeb714ff 100644
--- a/paper/paper.tex
+++ b/paper/paper.tex
@@ -272,7 +272,7 @@ As noted by \citet{guidotti2022counterfactual}, these distance-based measures ar
   \item Coverage and temperature impacts CCE in somewhat unpredictable ways
   \item It seems that models that are not explicitly trained for generative task, still learn it implictly
   \item Batch size seems to impact quality of generated samples (at inference, but not so much during JEM training)
-  \item ECCCo is sensitive to optimizer (Adam works well), learning rate and distance metric (l1 works well)
+  \item ECCCo is sensitive to optimizer (Adam works well), learning rate and distance metric (l1 currently only one that works)
   \item SGLD takes time 
   \item REVISE has benefit of lower dimensional space
 \end{itemize}
diff --git a/src/generator.jl b/src/generator.jl
index 87b1dceff3e45e92075efdfb5a69f614eb15e2af..72952aa31de89bf7d1a8d297a8337abe97cf9a16 100644
--- a/src/generator.jl
+++ b/src/generator.jl
@@ -28,7 +28,7 @@ function ECCCoGenerator(;
     function _set_size_penalty(ce::AbstractCounterfactualExplanation)
         return ECCCo.set_size_penalty(ce; κ=κ, temp=temp)
     end
-    _penalties = [Objectives.distance_l1, _set_size_penalty, ECCCo.distance_from_energy]
+    _penalties = [Objectives.distance_l2, _set_size_penalty, ECCCo.distance_from_energy]
     λ = λ isa AbstractFloat ? [0.0, λ, λ] : λ
     return Generator(; penalty=_penalties, λ=λ, opt=opt, kwargs...)
 end