diff --git a/docs/train.md b/docs/train.md
index 5da0ded8b8..80fa7bcac8 100644
--- a/docs/train.md
+++ b/docs/train.md
@@ -267,4 +267,4 @@ Because we use zero convolutions, the SD should always be able to predict meanin
 
 You will always find that at some iterations, the model "suddenly" be able to fit some training conditions. This means that you will get a basically usable model at about 3k to 7k steps (future training will improve it, but that model after the first "sudden converge" should be basically functional).
 
-Note that 3k to 7k steps is not very large, and you should consider larger batch size rather than more training steps. If you can observe the "sudden converge" at 3k step using batch size 4, rather than train it with 300k steps, a better idea is to use 100× gradient accumulation to re-train that 3k steps with 100× batch size. Note that perhaps we should not do this *too* extremely, but you should consider that, since "sudden converge" will always happen at some point, getting a better converge is more important.
+Note that 3k to 7k steps is not very large, and you should consider larger batch size rather than more training steps. If you can observe the "sudden converge" at 3k step using batch size 4, then, rather than train it with 300k further steps, a better idea is to use 100× gradient accumulation to re-train that 3k steps with 100× batch size. Note that perhaps we should not do this *too* extremely, but you should consider that, since "sudden converge" will always happen at some point, getting a better converge is more important.