diff --git a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
index 0e0414cf5c..cff0b66736 100644
--- a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
+++ b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
@@ -64,4 +64,8 @@
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
   </ItemGroup>
+
+  <ItemGroup>
+    <Folder Include="Properties\" />
+  </ItemGroup>
 </Project>
diff --git a/test/Microsoft.ML.Tests/Properties/AssemblyInfo.cs b/test/Microsoft.ML.Tests/Properties/AssemblyInfo.cs
deleted file mode 100644
index c8afa8dcba..0000000000
--- a/test/Microsoft.ML.Tests/Properties/AssemblyInfo.cs
+++ /dev/null
@@ -1,9 +0,0 @@
-// Licensed to the .NET Foundation under one or more agreements.
-// The .NET Foundation licenses this file to you under the MIT license.
-// See the LICENSE file in the project root for more information.
-
-using Xunit;
-
-// TODO: [TEST_STABILITY] disable test parallelization for this assembly as running test in parallel sometimes cause test host process to crash
-[assembly: CollectionBehavior(DisableTestParallelization = true)]
-
diff --git a/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs b/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs
index 857e642be9..6a1b0cba6c 100644
--- a/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs
+++ b/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs
@@ -811,12 +811,13 @@ private void ExecuteTFTransformMNISTConvTrainingTest(bool shuffle, int? shuffleS
                         batchSize: 20))
                     .Append(mlContext.Transforms.Concatenate("Features", "Prediction"))
                     .AppendCacheCheckpoint(mlContext)
+                    // Attention: Do not set NumberOfThreads here, left this to use default value to avoid test crash.
+                    // Details can be found here: https://github.com/dotnet/machinelearning/pull/4918
                     .Append(mlContext.MulticlassClassification.Trainers.LightGbm(new Trainers.LightGbm.LightGbmMulticlassTrainer.Options()
                     {
                         LabelColumnName = "Label",
                         FeatureColumnName = "Features",
                         Seed = 1,
-                        NumberOfThreads = 1,
                         NumberOfIterations = 1
                     }));
 
diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs b/test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs
index c119e33c99..e503133e91 100644
--- a/test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs
+++ b/test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs
@@ -52,10 +52,11 @@ public void LightGBMBinaryEstimator()
         {
             var (pipe, dataView) = GetBinaryClassificationPipeline();
 
+            // Attention: Do not set NumberOfThreads here, left this to use default value to avoid test crash.
+            // Details can be found here: https://github.com/dotnet/machinelearning/pull/4918
             var trainer = ML.BinaryClassification.Trainers.LightGbm(new LightGbmBinaryTrainer.Options
             {
                 NumberOfLeaves = 10,
-                NumberOfThreads = 1,
                 MinimumExampleCountPerLeaf = 2,
                 UnbalancedSets = false, // default value
             });
@@ -73,10 +74,11 @@ public void LightGBMBinaryEstimatorUnbalanced()
         {
             var (pipe, dataView) = GetBinaryClassificationPipeline();
 
+            // Attention: Do not set NumberOfThreads here, left this to use default value to avoid test crash.
+            // Details can be found here: https://github.com/dotnet/machinelearning/pull/4918
             var trainer = ML.BinaryClassification.Trainers.LightGbm(new LightGbmBinaryTrainer.Options
             {
                 NumberOfLeaves = 10,
-                NumberOfThreads = 1,
                 MinimumExampleCountPerLeaf = 2,
                 UnbalancedSets = true,
             });
@@ -98,10 +100,11 @@ public void LightGBMBinaryEstimatorCorrectSigmoid()
             var (pipe, dataView) = GetBinaryClassificationPipeline();
             var sigmoid = .789;
 
+            // Attention: Do not set NumberOfThreads here, left this to use default value to avoid test crash.
+            // Details can be found here: https://github.com/dotnet/machinelearning/pull/4918
             var trainer = ML.BinaryClassification.Trainers.LightGbm(new LightGbmBinaryTrainer.Options
             {
                 NumberOfLeaves = 10,
-                NumberOfThreads = 1,
                 MinimumExampleCountPerLeaf = 2,
                 Sigmoid = sigmoid
             });
@@ -218,9 +221,11 @@ public void FastTreeRegressorEstimator()
         public void LightGBMRegressorEstimator()
         {
             var dataView = GetRegressionPipeline();
+
+            // Attention: Do not set NumberOfThreads here, left this to use default value to avoid test crash.
+            // Details can be found here: https://github.com/dotnet/machinelearning/pull/4918
             var trainer = ML.Regression.Trainers.LightGbm(new LightGbmRegressionTrainer.Options
             {
-                NumberOfThreads = 1,
                 NormalizeFeatures = NormalizeOption.Warn,
                 L2CategoricalRegularization = 5,
             });
@@ -930,8 +935,15 @@ public void FastTreeTweedieRegressorTestSummary()
         public void LightGbmRegressorTestSummary()
         {
             var dataView = GetRegressionPipeline();
+
+            // Attention: Do not set NumberOfThreads here, left this to use default value to avoid test crash.
+            // Details can be found here: https://github.com/dotnet/machinelearning/pull/4918
             var trainer = ML.Regression.Trainers.LightGbm(
-                new LightGbmRegressionTrainer.Options { NumberOfIterations = 10, NumberOfThreads = 1, NumberOfLeaves = 5});
+                new LightGbmRegressionTrainer.Options 
+                { 
+                    NumberOfIterations = 10, 
+                    NumberOfLeaves = 5 
+                });
 
             var transformer = trainer.Fit(dataView);
 
@@ -984,8 +996,16 @@ public void FastForestBinaryClassificationTestSummary()
         public void LightGbmBinaryClassificationTestSummary()
         {
             var (pipeline, dataView) = GetOneHotBinaryClassificationPipeline();
+
+            // Attention: Do not set NumberOfThreads here, left this to use default value to avoid test crash.
+            // Details can be found here: https://github.com/dotnet/machinelearning/pull/4918
             var trainer = pipeline.Append(ML.BinaryClassification.Trainers.LightGbm(
-                new LightGbmBinaryTrainer.Options { NumberOfIterations = 10, NumberOfThreads = 1, NumberOfLeaves = 5, UseCategoricalSplit = true }));
+                new LightGbmBinaryTrainer.Options 
+                { 
+                    NumberOfIterations = 10, 
+                    NumberOfLeaves = 5, 
+                    UseCategoricalSplit = true 
+                }));
 
             var transformer = trainer.Fit(dataView);