diff --git a/tensorflow/compiler/xla/g3doc/_book.yaml b/tensorflow/compiler/xla/g3doc/_book.yaml
index e05f69b1e8b..8d217b89ae3 100644
--- a/tensorflow/compiler/xla/g3doc/_book.yaml
+++ b/tensorflow/compiler/xla/g3doc/_book.yaml
@@ -17,6 +17,8 @@ upper_tabs:
         path: /xla
       - title: XLA architecture
         path: /xla/architecture
+      - title: Known issues
+        path: /xla/known_issues
       - title: Broadcasting semantics
         path: /xla/broadcasting
       - title: Develop a new backend for XLA
diff --git a/tensorflow/compiler/xla/g3doc/index.md b/tensorflow/compiler/xla/g3doc/index.md
index 60bde306266..51d666fba9a 100644
--- a/tensorflow/compiler/xla/g3doc/index.md
+++ b/tensorflow/compiler/xla/g3doc/index.md
@@ -177,30 +177,6 @@ a bug to a single XLA program by using the
 [`replay_computation`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/tools/run_hlo_module_main.cc)
 and iteratively running it on generated programs.
 
-## Known Issues
-
-Compilation with XLA can greatly improve the performance of your programs, but
-the TensorFlow interop has a number of known sharp corners.
-
-### TensorArray TF/XLA Interconversion
-
-The problem manifests itself as an error message
-`Support for TensorList crossing the XLA/TF boundary is not implemented`.
-
-XLA supports `tf.TensorArray`. However, the _interconversion_ between TF and
-XLA representations is not implemented yet.
-This error often arises when the `TensorArray` is used inside the compiled
-block, but the derivative is taken outside.
-
-Workaround: compile the outermost scope which is taking the derivative.
-
-### Random Number Generation
-
-XLA currently ignores TF seeds to random operations. This affects stateful TF
-random operations, such as `tf.random.normal`, or `tf.nn.dropout`.  XLA will
-behave as if the compilation was seeded with a new unique seed at each run. This
-limitation does not apply to stateless random ops.
-
 ## XLA Frontends
 
 Apart from TensorFlow, XLA programs can be generated by:
diff --git a/tensorflow/compiler/xla/g3doc/known_issues.md b/tensorflow/compiler/xla/g3doc/known_issues.md
new file mode 100644
index 00000000000..1c03c716a02
--- /dev/null
+++ b/tensorflow/compiler/xla/g3doc/known_issues.md
@@ -0,0 +1,32 @@
+# Known Issues
+
+Compilation with XLA can greatly improve the performance of your programs, but
+the TensorFlow interop has a number of known sharp corners.
+
+## TensorArray TF/XLA interconversion
+
+The problem manifests itself as an error message
+`Support for TensorList crossing the XLA/TF boundary is not implemented`.
+
+XLA supports `tf.TensorArray`. However, the _interconversion_ between TF and
+XLA representations is not implemented yet.
+This error often arises when the `TensorArray` is used inside the compiled
+block, but the derivative is taken outside.
+
+Workaround: compile the outermost scope which is taking the derivative.
+
+## Dynamic `tf.TensorArray` is not supported
+
+Writes into `tf.TensorArray(..., dynamic_size=True)` are not compilable with
+XLA, as such writes require an unknown number of reallocations when the array
+exceeds the original bound.
+
+Workaround: provide a statically known bound to your arrays.
+
+## Random number generation
+
+XLA currently ignores TF seeds to random operations. This affects stateful TF
+random operations, such as `tf.random.normal`, or `tf.nn.dropout`.  XLA will
+behave as if the compilation was seeded with a new unique seed at each run. This
+limitation does not apply to stateless random ops.
+