diff --git a/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala b/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
index d31810098..038a65889 100644
--- a/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
+++ b/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
@@ -258,20 +258,20 @@ object XGBoost extends Serializable {
             case true => {
               val partitionedData = repartitionForTrainingGroup(trainingData, nWorkers)
               partitionedData.mapPartitions(labeledPointGroups => {
-                val watches = Watches.buildWatchesWithGroup(params,
+                val watches = Watches.buildWatchesWithGroup(overriddenParams,
                   removeMissingValuesWithGroup(labeledPointGroups, missing),
                   getCacheDirName(useExternalMemory))
-                buildDistributedBooster(watches, params, rabitEnv, checkpointRound,
+                buildDistributedBooster(watches, overriddenParams, rabitEnv, checkpointRound,
                   obj, eval, prevBooster)
               }).cache()
             }
             case false => {
               val partitionedData = repartitionForTraining(trainingData, nWorkers)
               partitionedData.mapPartitions(labeledPoints => {
-                val watches = Watches.buildWatches(params,
+                val watches = Watches.buildWatches(overriddenParams,
                   removeMissingValues(labeledPoints, missing),
                   getCacheDirName(useExternalMemory))
-                 buildDistributedBooster(watches, params, rabitEnv, checkpointRound,
+                 buildDistributedBooster(watches, overriddenParams, rabitEnv, checkpointRound,
                    obj, eval, prevBooster)
               }).cache()
             }
@@ -341,6 +341,9 @@ object XGBoost extends Serializable {
       // Copies of the final booster and the corresponding metrics
       // reside in each partition of the `distributedBoostersAndMetrics`.
       // Any of them can be used to create the model.
+      // it's safe to block here forever, as the tracker has returned successfully, and the Spark
+      // job should have finished, there is no reason for the thread cannot return
+      sparkJobThread.join()
       val (booster, metrics) = distributedBoostersAndMetrics.first()
       distributedBoostersAndMetrics.unpersist(false)
       (booster, metrics)