diff --git a/docs/en/macros/train-args.md b/docs/en/macros/train-args.md
index ede32f910b..0bc48f8117 100644
--- a/docs/en/macros/train-args.md
+++ b/docs/en/macros/train-args.md
@@ -17,7 +17,6 @@
 | `exist_ok`        | `False`  | If True, allows overwriting of an existing project/name directory. Useful for iterative experimentation without needing to manually clear previous outputs.                                                                                                  |
 | `pretrained`      | `True`   | Determines whether to start training from a pretrained model. Can be a boolean value or a string path to a specific model from which to load weights. Enhances training efficiency and model performance.                                                    |
 | `optimizer`       | `'auto'` | Choice of optimizer for training. Options include `SGD`, `Adam`, `AdamW`, `NAdam`, `RAdam`, `RMSProp` etc., or `auto` for automatic selection based on model configuration. Affects convergence speed and stability.                                         |
-| `verbose`         | `False`  | Enables verbose output during training, providing detailed logs and progress updates. Useful for debugging and closely monitoring the training process.                                                                                                      |
 | `seed`            | `0`      | Sets the random seed for training, ensuring reproducibility of results across runs with the same configurations.                                                                                                                                             |
 | `deterministic`   | `True`   | Forces deterministic algorithm use, ensuring reproducibility but may affect performance and speed due to the restriction on non-deterministic algorithms.                                                                                                    |
 | `single_cls`      | `False`  | Treats all classes in multi-class datasets as a single class during training. Useful for binary classification tasks or when focusing on object presence rather than classification.                                                                         |