diff --git a/docs/en/macros/train-args.md b/docs/en/macros/train-args.md
index d6ccfa47..69f9ce92 100644
--- a/docs/en/macros/train-args.md
+++ b/docs/en/macros/train-args.md
@@ -22,6 +22,7 @@
 | `single_cls`      | `bool`                   | `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.                                                                         |
 | `classes`         | `list[int]`              | `None`   | Specifies a list of class IDs to train on. Useful for filtering out and focusing only on certain classes during training.                                                                                                                                    |
 | `rect`            | `bool`                   | `False`  | Enables rectangular training, optimizing batch composition for minimal padding. Can improve efficiency and speed but may affect model accuracy.                                                                                                              |
+| `multi_scale`     | `bool`                   | `False`  | Enables multi-scale training by increasing/decreasing `imgsz` by upto a factor of `0.5` during training. Trains the model to be more accurate with multiple `imgsz` during inference.                                                                        |
 | `cos_lr`          | `bool`                   | `False`  | Utilizes a cosine [learning rate](https://www.ultralytics.com/glossary/learning-rate) scheduler, adjusting the learning rate following a cosine curve over epochs. Helps in managing learning rate for better convergence.                                   |
 | `close_mosaic`    | `int`                    | `10`     | Disables mosaic [data augmentation](https://www.ultralytics.com/glossary/data-augmentation) in the last N epochs to stabilize training before completion. Setting to 0 disables this feature.                                                                |
 | `resume`          | `bool`                   | `False`  | Resumes training from the last saved checkpoint. Automatically loads model weights, optimizer state, and epoch count, continuing training seamlessly.                                                                                                        |
@@ -42,7 +43,7 @@
 | `pose`            | `float`                  | `12.0`   | Weight of the pose loss in models trained for pose estimation, influencing the emphasis on accurately predicting pose keypoints.                                                                                                                             |
 | `kobj`            | `float`                  | `2.0`    | Weight of the keypoint objectness loss in pose estimation models, balancing detection confidence with pose accuracy.                                                                                                                                         |
 | `nbs`             | `int`                    | `64`     | Nominal batch size for normalization of loss.                                                                                                                                                                                                                |
-| `overlap_mask`    | `bool`                   | `True`   | Determines whether object masks should be merged into a single mask for training, or kept separate for each object. In case of overlap, the smaller mask is overlayed on top of the larger mask during merge.                                                |
+| `overlap_mask`    | `bool`                   | `True`   | Determines whether object masks should be merged into a single mask for training, or kept separate for each object. In case of overlap, the smaller mask is overlaid on top of the larger mask during merge.                                                 |
 | `mask_ratio`      | `int`                    | `4`      | Downsample ratio for segmentation masks, affecting the resolution of masks used during training.                                                                                                                                                             |
 | `dropout`         | `float`                  | `0.0`    | Dropout rate for regularization in classification tasks, preventing overfitting by randomly omitting units during training.                                                                                                                                  |
 | `val`             | `bool`                   | `True`   | Enables validation during training, allowing for periodic evaluation of model performance on a separate dataset.                                                                                                                                             |