diff --git a/R/LearnerTorch.R b/R/LearnerTorch.R
index ce2248ab..7536fd03 100644
--- a/R/LearnerTorch.R
+++ b/R/LearnerTorch.R
@@ -21,7 +21,7 @@
 #' After loading a marshaled `LearnerTorch` into R again, you then need to call `$unmarshal()` to transform it
 #' into a useable state.
 #'
-#' @section Early Stopping and Tuning:
+#' @section Early Stopping and Internal Tuning:
 #' In order to prevent overfitting, the `LearnerTorch` class allows to use early stopping via the `patience`
 #' and `min_delta` parameters, see the `Learner`'s parameters.
 #' When tuning a `LearnerTorch` it is also possible to combine the explicit tuning via `mlr3tuning`
diff --git a/man/mlr_learners.torchvision.Rd b/man/mlr_learners.torchvision.Rd
index 29acfb0e..87883dd9 100644
--- a/man/mlr_learners.torchvision.Rd
+++ b/man/mlr_learners.torchvision.Rd
@@ -90,9 +90,9 @@ Krizhevsky, Alex, Sutskever, Ilya, Hinton, E. G (2017).
 Sandler, Mark, Howard, Andrew, Zhu, Menglong, Zhmoginov, Andrey, Chen, Liang-Chieh (2018).
 \dQuote{Mobilenetv2: Inverted residuals and linear bottlenecks.}
 In \emph{Proceedings of the IEEE conference on computer vision and pattern recognition}, 4510--4520.
-He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, Sun, Jian (2016 ).
-\dQuote{Deep residual learning for image recognition .}
-In \emph{Proceedings of the IEEE conference on computer vision and pattern recognition }, 770--778 .
+He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, Sun, Jian (2016).
+\dQuote{Deep residual learning for image recognition.}
+In \emph{Proceedings of the IEEE conference on computer vision and pattern recognition}, 770--778.
 Simonyan, Karen, Zisserman, Andrew (2014).
 \dQuote{Very deep convolutional networks for large-scale image recognition.}
 \emph{arXiv preprint arXiv:1409.1556}.}
diff --git a/man/mlr_learners_torch.Rd b/man/mlr_learners_torch.Rd
index ebdfe53d..3c5213a9 100644
--- a/man/mlr_learners_torch.Rd
+++ b/man/mlr_learners_torch.Rd
@@ -29,7 +29,7 @@ After loading a marshaled \code{LearnerTorch} into R again, you then need to cal
 into a useable state.
 }
 
-\section{Early Stopping and Tuning}{
+\section{Early Stopping and Internal Tuning}{
 
 In order to prevent overfitting, the \code{LearnerTorch} class allows to use early stopping via the \code{patience}
 and \code{min_delta} parameters, see the \code{Learner}'s parameters.
diff --git a/man/mlr_learners_torch_image.Rd b/man/mlr_learners_torch_image.Rd
index fce0bef7..0797ba25 100644
--- a/man/mlr_learners_torch_image.Rd
+++ b/man/mlr_learners_torch_image.Rd
@@ -64,7 +64,7 @@ Creates a new instance of this \link[R6:R6Class]{R6} class.
   optimizer = NULL,
   loss = NULL,
   callbacks = list(),
-  packages = c("torchvision"),
+  packages = "torchvision",
   man,
   properties = NULL,
   predict_types = NULL