Docstring nits

keras_core/trainers/trainer.py

@@ -88,17 +88,18 @@ def compile(
                 `keras_core.metrics.BinaryAccuracy`,
                 `keras_core.metrics.CategoricalAccuracy`,
                 `keras_core.metrics.SparseCategoricalAccuracy` based on the
-                shapes of the targets and of the model output. We do a similar
-                conversion for the strings 'crossentropy' and 'ce' as well.
+                shapes of the targets and of the model output. A similar
+                conversion is done for the strings `"crossentropy"`
+                and `"ce"` as well.
                 The metrics passed here are evaluated without sample weighting;
                 if you would like sample weighting to apply, you can specify
                 your metrics via the `weighted_metrics` argument instead.
             weighted_metrics: List of metrics to be evaluated and weighted by
                 `sample_weight` or `class_weight` during training and testing.
-            run_eagerly: Bool. If `True`, this `Model`'s logic will never be
-                compiled (e.g. with `tf.function` or `jax.jit`). Recommended to
-                leave this as `False` when training for best performance, and
-                `True` when debugging.
+            run_eagerly: Bool. If `True`, this model's forward pass
+                 will never be compiled. It is recommended to leave this
+                 as `False` when training (for best performance),
+                 and to set it to `True` when debugging.
             steps_per_execution: Int. The number of batches to run
                 during each a single compiled function call. Running multiple
                 batches inside a single a single compiled function call can
@@ -110,15 +111,11 @@ def compile(
                 `Callback.on_batch_begin` and `Callback.on_batch_end` methods
                 will only be called every `N` batches (i.e. before/after
                 each compiled function execution).
+                Not supported with the PyTorch backend.
             jit_compile: Bool or `"auto"`. Whether to use XLA compilation when
-                compiling a model. This value should currently never be `True`
-                on the torch backed, and should always be `True` or `"auto"` on
-                the jax backend. On tensorflow, this value can be `True` or
-                `False`, and will toggle the `jit_compile` option for any
-                `tf.function` owned by the model. See
-                https://www.tensorflow.org/xla/tutorials/jit_compile for more
-                details. If `"auto"`, XLA compilation will be enabled if the
-                backend supports it, and disabled otherwise.
+                compiling a model. Not supported with the PyTorch backend.
+                If `"auto"`, XLA compilation will be enabled if the
+                the model supports it, and disabled otherwise.
         """
         self.optimizer = optimizers.get(optimizer)
         if hasattr(self, "output_names"):