diff --git a/src/mlpack/methods/random_forest/random_forest_impl.hpp b/src/mlpack/methods/random_forest/random_forest_impl.hpp
index 0277f71ece..106072c8f2 100644
--- a/src/mlpack/methods/random_forest/random_forest_impl.hpp
+++ b/src/mlpack/methods/random_forest/random_forest_impl.hpp
@@ -476,21 +476,15 @@ double RandomForest<
          DimensionSelectionType& dimensionSelector,
          const bool warmStart)
 {
-  size_t oldNumTrees = trees.size();
+  // Reset the forest if we are not doing a warm-start.
+  if (!warmStart)
+    trees.clear();
+  const size_t oldNumTrees = trees.size();
+  trees.resize(trees.size() + numTrees);
+
   // Convert avgGain to total gain.
   double totalGain = avgGain * oldNumTrees;
 
-  if (warmStart)
-  {
-    // This will extend the vector with untrained trees.
-    trees.resize(trees.size() + numTrees);
-  }
-  else
-  {
-    // This will fill the vector with untrained trees.
-    trees.resize(numTrees);
-  }
-
   // Train each tree individually.
   #pragma omp parallel for reduction( + : totalGain)
   for (omp_size_t i = 0; i < numTrees; ++i)
@@ -503,45 +497,39 @@ double RandomForest<
         bootstrapLabels, bootstrapWeights);
     Timer::Stop("bootstrap");
 
-    // Now build the decision tree.
-    DecisionTreeType tmpTree;
     Timer::Start("train_tree");
     if (UseWeights)
     {
       if (UseDatasetInfo)
       {
-        totalGain += tmpTree.Train(bootstrapDataset, datasetInfo,
-            bootstrapLabels, numClasses, bootstrapWeights, minimumLeafSize,
-            minimumGainSplit, maximumDepth, dimensionSelector);
+        totalGain += trees[oldNumTrees + i].Train(bootstrapDataset,
+            datasetInfo, bootstrapLabels, numClasses, bootstrapWeights,
+            minimumLeafSize, minimumGainSplit, maximumDepth,
+            dimensionSelector);
       }
       else
       {
-        totalGain += tmpTree.Train(bootstrapDataset, bootstrapLabels, numClasses,
-            bootstrapWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
-            dimensionSelector);
+        totalGain += trees[oldNumTrees + i].Train(bootstrapDataset,
+            bootstrapLabels, numClasses, bootstrapWeights, minimumLeafSize,
+            minimumGainSplit, maximumDepth, dimensionSelector);
       }
     }
     else
     {
       if (UseDatasetInfo)
       {
-        totalGain += tmpTree.Train(bootstrapDataset, datasetInfo,
-            bootstrapLabels, numClasses, minimumLeafSize, minimumGainSplit,
-            maximumDepth, dimensionSelector);
+        totalGain += trees[oldNumTrees + i].Train(bootstrapDataset,
+            datasetInfo, bootstrapLabels, numClasses, minimumLeafSize,
+            minimumGainSplit, maximumDepth, dimensionSelector);
       }
       else
       {
-        totalGain += tmpTree.Train(bootstrapDataset, bootstrapLabels, numClasses,
-            minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
+        totalGain += trees[oldNumTrees + i].Train(bootstrapDataset,
+            bootstrapLabels, numClasses, minimumLeafSize, minimumGainSplit,
+            maximumDepth, dimensionSelector);
       }
     }
 
-    // Storing the trained tree at the desired index.
-    if (warmStart)
-      trees[oldNumTrees + i] = tmpTree;
-    else
-      trees[i] = tmpTree;
-
     Timer::Stop("train_tree");
   }