diff --git a/benchmarks/benchmark_multimodal.jl b/benchmarks/benchmark_multimodal.jl
index eb2ef6f..1fe5513 100644
--- a/benchmarks/benchmark_multimodal.jl
+++ b/benchmarks/benchmark_multimodal.jl
@@ -17,7 +17,7 @@ include("benchmark_utils.jl")
             hparams = HyperParamsVanillaGan(;
                 data_size=100,
                 batch_size=1,
-                epochs=1e3,
+                epochs=1e4,
                 lr_dscr=1e-4,
                 lr_gen=1e-4,
                 dscr_steps=0,
@@ -29,7 +29,7 @@ include("benchmark_utils.jl")
             train_vanilla_gan(dscr, gen, hparams)
 
             hparams = HyperParams(;
-                samples=1000, K=100, epochs=100, η=1e-2, transform=noise_model
+                samples=200, K=10, epochs=1000, η=2e-2, transform=noise_model
             )
             #hparams = AutoAdaptativeHyperParams(;
             #    max_k=20, samples=1200, epochs=10000, η=1e-3, transform=noise_model
@@ -59,7 +59,7 @@ include("benchmark_utils.jl")
                 gen,
                 n_samples,
                 (-3:0.1:3),
-                (5:0.2:15),
+                (-8:0.2:8),
             )
 
             #@test js_divergence(hist1.weights, hist2.weights)/hparams.samples < 0.03
diff --git a/test/runtests.jl b/test/runtests.jl
index e19d772..636b8d3 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -88,7 +88,7 @@ end;
 @testset "adaptative_block_learning" begin
     @testset "learning Normal(4.0f0, 2.0f0)" begin
         nn = Chain(Dense(1, 7), elu, Dense(7, 13), elu, Dense(13, 7), elu, Dense(7, 1))
-        hparams = HyperParams(1000, 10, 400, 1e-2, Normal(0.0f0, 1.0f0))
+        hparams = HyperParams(1000, 10, 1000, 1e-2, Normal(0.0f0, 1.0f0))
 
         function real_model(ϵ)
             return rand(Normal(4.0f0, 2.0f0))
@@ -112,7 +112,7 @@ end;
 
     @testset "learning uniform distribution (1,3)" begin
         nn = Chain(Dense(1, 7), elu, Dense(7, 13), elu, Dense(13, 7), elu, Dense(7, 1))
-        hparams = HyperParams(100, 10, 1000, 1e-2, Normal(0.0f0, 1.0f0))
+        hparams = HyperParams(100, 10, 2000, 1e-2, Normal(0.0f0, 1.0f0))
 
         function real_model(ϵ)
             return rand(Float32) * 2 + 1
@@ -132,7 +132,7 @@ end;
 
     @testset "learning Cauchy distribution" begin
         nn = Chain(Dense(1, 7), elu, Dense(7, 13), elu, Dense(13, 7), elu, Dense(7, 1))
-        hparams = HyperParams(100, 10, 1000, 1e-3, Normal(0.0f0, 1.0f0))
+        hparams = HyperParams(100, 10, 2000, 1e-3, Normal(0.0f0, 1.0f0))
 
         function real_model(ϵ)
             return rand(Cauchy(1.0f0, 2.0f0))
@@ -152,7 +152,7 @@ end;
 
     @testset "learning Bimodal Normal Distribution" begin
         nn = Chain(Dense(1, 7), elu, Dense(7, 13), elu, Dense(13, 7), elu, Dense(7, 1))
-        hparams = HyperParams(100, 10, 2000, 1e-2, Normal(0.0f0, 1.0f0))
+        hparams = HyperParams(100, 10, 4000, 1e-2, Normal(0.0f0, 1.0f0))
 
         function real_model(ϵ)
             return rand(MixtureModel(Normal[Normal(5.0f0, 2.0f0), Normal(-1.0f0, 1.0f0)]))
@@ -173,7 +173,7 @@ end;
     @testset "learning modal auto_adaptative_block_learning Normal(4.0f0, 2.0f0)" begin
         nn = Chain(Dense(1, 7), elu, Dense(7, 13), elu, Dense(13, 7), elu, Dense(7, 1))
         hparams = AutoAdaptativeHyperParams(;
-            max_k=10, samples=1000, epochs=400, η=1e-2, transform=Normal(0.0f0, 1.0f0)
+            max_k=10, samples=1000, epochs=1000, η=1e-2, transform=Normal(0.0f0, 1.0f0)
         )
 
         function real_model(ϵ)