diff --git a/docs/compatibility.md b/docs/compatibility.md
index 9d411f56d50..4e1d604b1ea 100644
--- a/docs/compatibility.md
+++ b/docs/compatibility.md
@@ -83,6 +83,18 @@ after Spark 3.1.0.
 We do not disable operations that produce different results due to `-0.0` in the data because it is
 considered to be a rare occurrence.
 
+### `NaN` vs `NaN`
+
+Apache Spark does not have a consistent way to handle `NaN` comparison. Sometimes, all `NaN` are
+considered as one unique value while other times they can be treated as different. The outcome of
+`NaN` comparison can differ in various operations and also changed between Spark versions.
+The RAPIDS Accelerator tries to match its output with Apache Spark except for a few operation(s) listed below:
+ - `IN` SQL expression: `NaN` can be treated as different values in Spark 3.1.2 and
+ prior versions, see [SPARK-36792](https://issues.apache.org/jira/browse/SPARK-36792) for more details.
+The RAPIDS Accelerator compares `NaN` values as equal for this operation which matches
+the behavior of Apache Spark 3.1.3 and later versions.
+
+
 ## Decimal Support
 
 Apache Spark supports decimal values with a precision up to 38. This equates to 128-bits.
diff --git a/integration_tests/src/main/python/cmp_test.py b/integration_tests/src/main/python/cmp_test.py
index 59759098f28..1e1549f28be 100644
--- a/integration_tests/src/main/python/cmp_test.py
+++ b/integration_tests/src/main/python/cmp_test.py
@@ -17,7 +17,7 @@
 from asserts import assert_gpu_and_cpu_are_equal_collect
 from conftest import is_not_utc
 from data_gen import *
-from spark_session import with_cpu_session, is_before_spark_330
+from spark_session import with_cpu_session, is_before_spark_313, is_before_spark_330
 from pyspark.sql.types import *
 from marks import datagen_overrides, allow_non_gpu
 import pyspark.sql.functions as f
@@ -335,11 +335,16 @@ def test_in(data_gen):
     assert_gpu_and_cpu_are_equal_collect(
             lambda spark : unary_op_df(spark, data_gen).select(f.col('a').isin(scalars)))
 
+# We avoid testing inset with NaN in Spark < 3.1.3 since it has issue with NaN comparisons.
+# See https://github.com/NVIDIA/spark-rapids/issues/9687.
+test_inset_data_gen = [gen for gen in eq_gens_with_decimal_gen if gen != float_gen if gen != double_gen] + \
+                                   [FloatGen(no_nans=True), DoubleGen(no_nans=True)] \
+                      if is_before_spark_313() else eq_gens_with_decimal_gen
+
 # Spark supports two different versions of 'IN', and it depends on the spark.sql.optimizer.inSetConversionThreshold conf
 # This is to test entries over that value.
-@datagen_overrides(seed=0, reason='https://github.com/NVIDIA/spark-rapids/issues/9687')
-@pytest.mark.parametrize('data_gen', eq_gens_with_decimal_gen, ids=idfn)
 @allow_non_gpu(*non_utc_allow)
+@pytest.mark.parametrize('data_gen', test_inset_data_gen, ids=idfn)
 def test_in_set(data_gen):
     # nulls are not supported for in on the GPU yet
     num_entries = int(with_cpu_session(lambda spark: spark.conf.get('spark.sql.optimizer.inSetConversionThreshold'))) + 1