diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index 73dd471ab1d52b718b7ac5addf90b3e4c4294374..d5f3e3f6ec496f948aad64a9a6a0f9664c8b6259 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -189,8 +189,8 @@ class GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Ser
  * A simpler value of [[Accumulable]] where the result type being accumulated is the same
  * as the types of elements being merged, i.e. variables that are only "added" to through an
  * associative operation and can therefore be efficiently supported in parallel. They can be used
- * to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of type
- * `Int` and `Double`, and programmers can add support for new types.
+ * to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of numeric
+ * value types, and programmers can add support for new types.
  *
  * An accumulator is created from an initial value `v` by calling [[SparkContext#accumulator]].
  * Tasks running on the cluster can then add to it using the [[Accumulable#+=]] operator.
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index cd847e07f94abe7a2b068fb325806c2051a40112..506d3faa767f3cc0b3420168c40b3955ebd41c6b 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -344,7 +344,7 @@ After the broadcast variable is created, it should be used instead of the value
 
 ## Accumulators
 
-Accumulators are variables that are only "added" to through an associative operation and can therefore be efficiently supported in parallel. They can be used to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of type Int and Double, and programmers can add support for new types.
+Accumulators are variables that are only "added" to through an associative operation and can therefore be efficiently supported in parallel. They can be used to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of numeric value types and standard mutable collections, and programmers can add support for new types.
 
 An accumulator is created from an initial value `v` by calling `SparkContext.accumulator(v)`. Tasks running on the cluster can then add to it using the `+=` operator. However, they cannot read its value. Only the driver program can read the accumulator's value, using its `value` method.