diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R
index cd13d82b172e..b20ab018b43f 100644
--- a/R-package/R/xgb.train.R
+++ b/R-package/R/xgb.train.R
@@ -427,7 +427,8 @@ xgb.train <- function(params = xgb.params(), data, nrounds, evals = list(),
 #' - `"binary:logistic"`: logistic regression for binary classification, output probability
 #' - `"binary:logitraw"`: logistic regression for binary classification, output score before logistic transformation
 #' - `"binary:hinge"`: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.
-#' - `"count:poisson"`: Poisson regression for count data, output mean of Poisson distribution. #' `"max_delta_step"` is set to 0.7 by default in Poisson regression (used to safeguard optimization)
+#' - `"count:poisson"`: Poisson regression for count data, output mean of Poisson distribution.
+#'   `"max_delta_step"` is set to 0.7 by default in Poisson regression (used to safeguard optimization)
 #' - `"survival:cox"`: Cox regression for right censored survival time data (negative values are considered right censored).
 #'
 #'   Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional hazard function `h(t) = h0(t) * HR`).
@@ -451,7 +452,7 @@ xgb.train <- function(params = xgb.params(), data, nrounds, evals = list(),
 #' @param seed Random number seed. If not specified, will take a random seed through R's own RNG engine.
 #' @param booster (default= `"gbtree"`)
 #' Which booster to use. Can be `"gbtree"`, `"gblinear"` or `"dart"`; `"gbtree"` and `"dart"` use tree based models while `"gblinear"` uses linear functions.
-#' @param eta,learning_rate (two aliases for the same parameter) (for Tree Booster) (default=0.3)
+#' @param eta,learning_rate (two aliases for the same parameter) (default=0.3)
 #' Step size shrinkage used in update to prevent overfitting. After each boosting step, we can directly get the weights of new features, and `eta` shrinks the feature weights to make the boosting process more conservative.
 #'
 #' range: \eqn{[0,1]}
diff --git a/R-package/man/xgb.params.Rd b/R-package/man/xgb.params.Rd
index 051fba6c8bd0..e5c74d85f837 100644
--- a/R-package/man/xgb.params.Rd
+++ b/R-package/man/xgb.params.Rd
@@ -90,7 +90,8 @@ Version added: 2.0.0
 \item \code{"binary:logistic"}: logistic regression for binary classification, output probability
 \item \code{"binary:logitraw"}: logistic regression for binary classification, output score before logistic transformation
 \item \code{"binary:hinge"}: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.
-\item \code{"count:poisson"}: Poisson regression for count data, output mean of Poisson distribution. #' \code{"max_delta_step"} is set to 0.7 by default in Poisson regression (used to safeguard optimization)
+\item \code{"count:poisson"}: Poisson regression for count data, output mean of Poisson distribution.
+\code{"max_delta_step"} is set to 0.7 by default in Poisson regression (used to safeguard optimization)
 \item \code{"survival:cox"}: Cox regression for right censored survival time data (negative values are considered right censored).
 
 Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional hazard function \code{h(t) = h0(t) * HR}).
@@ -120,7 +121,7 @@ contention and hyperthreading in mind.}
 \item{booster}{(default= \code{"gbtree"})
 Which booster to use. Can be \code{"gbtree"}, \code{"gblinear"} or \code{"dart"}; \code{"gbtree"} and \code{"dart"} use tree based models while \code{"gblinear"} uses linear functions.}
 
-\item{eta, learning_rate}{(two aliases for the same parameter) (for Tree Booster) (default=0.3)
+\item{eta, learning_rate}{(two aliases for the same parameter) (default=0.3)
 Step size shrinkage used in update to prevent overfitting. After each boosting step, we can directly get the weights of new features, and \code{eta} shrinks the feature weights to make the boosting process more conservative.
 
 range: \eqn{[0,1]}