diff --git a/keras_core/backend/torch/random.py b/keras_core/backend/torch/random.py
index 77e024411..5b5dfda74 100644
--- a/keras_core/backend/torch/random.py
+++ b/keras_core/backend/torch/random.py
@@ -30,8 +30,9 @@ def categorical(logits, num_samples, dtype="int32", seed=None):
     logits = convert_to_tensor(logits)
     dtype = to_torch_dtype(dtype)
     generator = torch_seed_generator(seed, device=get_device())
+    probs = torch.softmax(logits, dim=-1)
     return torch.multinomial(
-        logits,
+        probs,
         num_samples,
         replacement=True,
         generator=generator,
diff --git a/keras_core/random/random_test.py b/keras_core/random/random_test.py
index 3267a7a79..dd8b45af0 100644
--- a/keras_core/random/random_test.py
+++ b/keras_core/random/random_test.py
@@ -48,8 +48,9 @@ class RandomTest(testing.TestCase, parameterized.TestCase):
     )
     def test_categorical(self, seed, num_samples, batch_size):
         np.random.seed(seed)
-        # Definitively favor the batch index.
-        logits = np.eye(batch_size) * 1e9
+        # Create logits that definitely favors the batch index after a softmax
+        # is applied. Without a softmax, this would be close to random.
+        logits = np.eye(batch_size) * 1e5 + 1e6
         res = random.categorical(logits, num_samples, seed=seed)
         # Outputs should have shape `(batch_size, num_samples)`, where each
         # output index matches the batch index.