diff --git a/src/Microsoft.ML.Mkl.Components/OlsLinearRegression.cs b/src/Microsoft.ML.Mkl.Components/OlsLinearRegression.cs
index 1d072c1290..af9a16574e 100644
--- a/src/Microsoft.ML.Mkl.Components/OlsLinearRegression.cs
+++ b/src/Microsoft.ML.Mkl.Components/OlsLinearRegression.cs
@@ -252,9 +252,13 @@ private void ComputeOneDalRegression(IChannel ch, FloatLabelCursor.Factory curso
                             {
                                 featuresArray[rowOffset * (m - 1) + k] = 0;
                             }
-                            featuresArray[rowOffset * (m - 1) + j] = values[indices[j]];
+                            featuresArray[rowOffset * (m - 1) + indices[j]] = values[j];
                             i = indices[j] + 1;
                         }
+                        for (int j = i; j < m - 1; ++j)
+                        {
+                            featuresArray[rowOffset * (m - 1) + j] = 0;
+                        }
                     }
                     n++;
                 }
diff --git a/src/Microsoft.ML.OneDal/OneDalUtils.cs b/src/Microsoft.ML.OneDal/OneDalUtils.cs
index 061c4a3a88..269cea75e9 100644
--- a/src/Microsoft.ML.OneDal/OneDalUtils.cs
+++ b/src/Microsoft.ML.OneDal/OneDalUtils.cs
@@ -72,9 +72,13 @@ internal static long GetTrainData(IChannel channel, FloatLabelCursor.Factory cur
                             {
                                 featuresList.Add(0);
                             }
-                            featuresList.Add(values[indices[j]]);
+                            featuresList.Add(values[j]);
                             i = indices[j] + 1;
                         }
+                        for (int j = i; j < numberOfFeatures; ++j)
+                        {
+                            featuresList.Add(0);
+                        }
                     }
                     n++;
                 }
diff --git a/src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/LbfgsPredictorBase.cs b/src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/LbfgsPredictorBase.cs
index c5008a76ca..713e457f6f 100644
--- a/src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/LbfgsPredictorBase.cs
+++ b/src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/LbfgsPredictorBase.cs
@@ -523,9 +523,13 @@ private protected virtual void TrainCoreOneDal(IChannel ch, RoleMappedData data)
                             {
                                 featuresList.Add(0);
                             }
-                            featuresList.Add(values[indices[j]]);
+                            featuresList.Add(values[j]);
                             i = indices[j] + 1;
                         }
+                        for (int j = i; j < nFeatures; ++j)
+                        {
+                            featuresList.Add(0);
+                        }
                     }
                 }
                 NumGoodRows = cursor.KeptRowCount;
diff --git a/src/Native/OneDalNative/OneDalAlgorithms.cpp b/src/Native/OneDalNative/OneDalAlgorithms.cpp
index a02656baea..f30830614f 100644
--- a/src/Native/OneDalNative/OneDalAlgorithms.cpp
+++ b/src/Native/OneDalNative/OneDalAlgorithms.cpp
@@ -570,7 +570,7 @@ void logisticRegressionLBFGSComputeTemplate(FPType * featuresPtr, int * labelsPt
     SharedPtr<optimization_solver::lbfgs::Batch<FPType>> lbfgsAlgorithm(new optimization_solver::lbfgs::Batch<FPType>());
     lbfgsAlgorithm->parameter.batchSize = featuresTable->getNumberOfRows();
     lbfgsAlgorithm->parameter.correctionPairBatchSize = featuresTable->getNumberOfRows();
-    lbfgsAlgorithm->parameter.L = 1;
+    lbfgsAlgorithm->parameter.L = 10;
     lbfgsAlgorithm->parameter.m = m;
     lbfgsAlgorithm->parameter.accuracyThreshold = accuracyThreshold;
     lbfgsAlgorithm->parameter.nIterations = nIterations;
@@ -642,6 +642,15 @@ void logisticRegressionLBFGSComputeTemplate(FPType * featuresPtr, int * labelsPt
 
     if (verbose)
     {
+        printf("Intercepts and coefficients:\n");
+        for (size_t i = 0; i < nClasses; ++i)
+        {
+            for (size_t j = 0; j < nColumns + 1; ++j)
+            {
+                printf("%f ", betaPtr[i * (nColumns + 1) + j]);
+            }
+            printf("\n");
+        }
         optimization_solver::iterative_solver::ResultPtr solverResult = lbfgsAlgorithm->getResult();
         NumericTablePtr nIterationsTable = solverResult->get(optimization_solver::iterative_solver::nIterations);
         BlockDescriptor<int> nIterationsBlock;