diff --git a/keras_core/backend/torch/__init__.py b/keras_core/backend/torch/__init__.py
index c7449978e..8bc9bddca 100644
--- a/keras_core/backend/torch/__init__.py
+++ b/keras_core/backend/torch/__init__.py
@@ -1,14 +1,17 @@
 """Torch backend APIs.
 
-Torch has a different logic of device management compared to TF and JAX. In
-short variables/tensors are not by default created on GPU, and GPU cannot
-directly communicate with CPU. Therefore, we are doing the following to automate
-device management for Torch backend, if GPU is available:
+# Note on device placement
+
+Torch has a different device placement style compared to TF and JAX.
+In short, variables/tensors are not created on GPU by default,
+and the GPU cannot directly communicate with the CPU.
+To bring Torch behavior in line with TF and JAX automated device placement,
+we are doing the following to automate device placement if a GPU is available:
 
 - Variables are created on GPU.
 - Input data will be placed on GPU at the first `keras_core.layers.Layer` call.
 - Tensor creation happens on GPU, e.g., `zeros()` will create a tensor on GPU.
-- `convert_to_numpy` will bring the tensor to CPU and convert to numpy array.
+- `convert_to_numpy` will bring the tensor to CPU before converting it to NumPy.
 """
 
 from keras_core.backend.torch import core
diff --git a/keras_core/operations/core.py b/keras_core/operations/core.py
index e8d8efdd1..bc66306d4 100644
--- a/keras_core/operations/core.py
+++ b/keras_core/operations/core.py
@@ -4,6 +4,11 @@ scatter_update
 slice
 slice_update
 while_loop
+stop_gradient
+shape
+cast
+convert_to_tensor
+convert_to_numpy
 """
 
 from keras_core import backend
diff --git a/keras_core/trainers/trainer.py b/keras_core/trainers/trainer.py
index 75447963e..5f8c29a0e 100644
--- a/keras_core/trainers/trainer.py
+++ b/keras_core/trainers/trainer.py
@@ -43,7 +43,7 @@ class Trainer:
         else:
             self._compile_metrics = None
         if jit_compile == "auto":
-            if model_supports_jit(self):
+            if not run_eagerly and model_supports_jit(self):
                 jit_compile = True
             else:
                 jit_compile = False