diff --git a/docs/basic_usage/sampling_params.md b/docs/basic_usage/sampling_params.md
index 8b1035b81..5b93349ab 100644
--- a/docs/basic_usage/sampling_params.md
+++ b/docs/basic_usage/sampling_params.md
@@ -61,6 +61,7 @@ python -m sglang.launch_server --model-path <MODEL> --sampling-defaults openai
 |--------------------|------------------------|------------------------------------------------------------------------------------------------------------------------------------------------|
 | frequency_penalty  | `float = 0.0`          | Penalizes tokens based on their frequency in generation so far. Must be between `-2` and `2` where negative numbers encourage repeatment of tokens and positive number encourages sampling of new tokens. The scaling of penalization grows linearly with each appearance of a token. |
 | presence_penalty   | `float = 0.0`          | Penalizes tokens if they appeared in the generation so far. Must be between `-2` and `2` where negative numbers encourage repeatment of tokens and positive number encourages sampling of new tokens. The scaling of the penalization is constant if a token occurred. |
+| repetition_penalty | `float = 1.0`          | Scales the logits of previously generated tokens to discourage (values > 1) or encourage (values < 1) repetition. Valid range is `[0, 2]`; `1.0` leaves probabilities unchanged. |
 | min_new_tokens     | `int = 0`              | Forces the model to generate at least `min_new_tokens` until a stop word or EOS token is sampled. Note that this might lead to unintended behavior, for example, if the distribution is highly skewed towards these tokens. |
 
 ### Constrained decoding